https://crtc.cs.odu.edu/api.php?action=feedcontributions&user=Nikos&feedformat=atomcrtc.cs.odu.edu - User contributions [en]2024-03-29T12:59:53ZUser contributionsMediaWiki 1.29.1https://crtc.cs.odu.edu/index.php?title=People/Nikos-Chrisochoides&diff=9296People/Nikos-Chrisochoides2024-03-10T11:05:09Z<p>Nikos: </p>
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<div>[[File:Nikos_CoS_ODU_2020.png|250px|thumb|left|frameless|CRTC Principal Investigator: Nikos Chrisochoides]]<br />
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<b> Short Bio </b>: Nikos Chrisochoides is the Richard T. Cheng Endowed Chair Professor of Computer Science (2010), an Eminent Scholar at Old Dominion University (2019), and a Visiting Professor in the Medical School at Aristotle University of Thessaloniki (AUTh), Greece (2023). In addition, he is a John Simon Guggenheim Fellow in Medicine & Health (2007) in the US and Canada. He was elected an SIAM IMR Fellow (2024) and Nuclear Femtography Fellow (2020) in the US and a Distinguished Visiting Fellow in the Royal Academy of Engineering in the UK (2012). Nikos received his Ph.D. in 1992 from Computer Science at Purdue University. From 1992 to 1997, he worked in Upstate NY, where he was the inaugural Alex Nason Fellow at Northeast Parallel Architectures Center in Syracuse and a Research Scientist at the Advanced Computing Research Institute at Cornell University. In 1997, he joined the Computer Science & Engineering Dept. at Notre Dame, where he received his NSF CAREER Award (1998). In 2000, he joined the College of William and Mary, where he was awarded the Alumni Memorial Professorship. He has held visiting positions at MIT, Harvard Medical School (2005), and Brown University (2004). He participated as PI, Co-I, and Senior Personnel on projects with more than $16 million (with more than $10M as a PI) in high-performance scientific and medical image computing, and he has about 250 publications.</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=People/Nikos-Chrisochoides&diff=9295People/Nikos-Chrisochoides2024-03-10T11:03:58Z<p>Nikos: </p>
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<div>[[File:Nikos_CoS_ODU_2020.png|250px|thumb|left|frameless|CRTC Principal Investigator: Nikos Chrisochoides]]<br />
<br />
<b> Short Bio </b>: Nikos Chrisochoides is the Richard T. Cheng Endowed Chair Professor of Computer Science (2010), an Eminent Scholar at Old Dominion University (2019), and a Visiting Professor in the Medical School at Aristotle University of Thessaloniki (AUTh), Greece (2023). In addition, he is a John Simon Guggenheim Fellow in Medicine & Health (2007) in the US and Canada. He was elected an SIAM IMR Fellow (2024) and Nuclear Femtography Fellow (2020) in the US and a Distinguished Visiting Fellow in the Royal Academy of Engineering in the UK (2012). Nikos received his Ph.D. in 1992 from Computer Science at Purdue University. From 1992 to 1997, he worked in Upstate NY, where he was the inaugural Alex Nason Fellow at Northeast Parallel Architectures Center in Syracuse (1992) and a Research Scientist at the Advanced Computing Research Institute at Cornell University (1995). In 1997, he joined the Computer Science & Engineering Dept. at Notre Dame, where he received his NSF CAREER Award (1998). In 2000, he joined the College of William and Mary, where he was awarded the Alumni Memorial Professorship. He has held visiting positions at MIT, Harvard Medical School (2005), and Brown University (2004). He participated as PI, Co-I, and Senior Personnel on projects with more than $16 million (with more than $10M as a PI) in high-performance scientific and medical image computing, and he has about 250 publications.</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=People/Nikos-Chrisochoides&diff=9294People/Nikos-Chrisochoides2024-03-10T11:03:36Z<p>Nikos: </p>
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<div>[[File:Nikos_CoS_ODU_2020.png|250px|thumb|left|frameless|CRTC Principal Investigator: Nikos Chrisochoides]]<br />
<br />
<b> Short Bio </b>: Nikos Chrisochoides is the Richard T. Cheng Endowed Chair Professor of Computer Science (2010), an Eminent Scholar at Old Dominion University (2019), and a Visiting Professor in the Medical School at Aristotle University of Thessaloniki (AUTh), Greece. In addition, he is a John Simon Guggenheim Fellow in Medicine & Health (2007) in the US and Canada. He was elected an SIAM IMR Fellow (2024) and Nuclear Femtography Fellow (2020) in the US and a Distinguished Visiting Fellow in the Royal Academy of Engineering in the UK (2012). Nikos received his Ph.D. in 1992 from Computer Science at Purdue University. From 1992 to 1997, he worked in Upstate NY, where he was the inaugural Alex Nason Fellow at Northeast Parallel Architectures Center in Syracuse (1992) and a Research Scientist at the Advanced Computing Research Institute at Cornell University (1995). In 1997, he joined the Computer Science & Engineering Dept. at Notre Dame, where he received his NSF CAREER Award (1998). In 2000, he joined the College of William and Mary, where he was awarded the Alumni Memorial Professorship. He has held visiting positions at MIT, Harvard Medical School (2005), and Brown University (2004). He participated as PI, Co-I, and Senior Personnel on projects with more than $16 million (with more than $10M as a PI) in high-performance scientific and medical image computing, and he has about 250 publications.</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=People/Nikos-Chrisochoides&diff=9293People/Nikos-Chrisochoides2024-03-10T11:02:52Z<p>Nikos: </p>
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<div>[[File:Nikos_CoS_ODU_2020.png|250px|thumb|left|frameless|CRTC Principal Investigator: Nikos Chrisochoides]]<br />
<br />
<b> Short Bio </b>: Nikos Chrisochoides is the Richard T. Cheng Endowed Chair Professor of Computer Science, an Eminent Scholar at Old Dominion University, and a Visiting Professor in the Medical School at Aristotle University of Thessaloniki (AUTh), Greece. In addition, he is a John Simon Guggenheim Fellow in Medicine & Health (2007) in the US and Canada. He was elected an SIAM IMR Fellow (2024) and Nuclear Femtography Fellow (2020) in the US and a Distinguished Visiting Fellow in the Royal Academy of Engineering in the UK (2012). Nikos received his Ph.D. in 1992 from Computer Science at Purdue University. From 1992 to 1997, he worked in Upstate NY, where he was the inaugural Alex Nason Fellow at Northeast Parallel Architectures Center in Syracuse (1992) and a Research Scientist at the Advanced Computing Research Institute at Cornell University (1995). In 1997, he joined the Computer Science & Engineering Dept. at Notre Dame, where he received his NSF CAREER Award (1998). In 2000, he joined the College of William and Mary, where he was awarded the Alumni Memorial Professorship. He has held visiting positions at MIT, Harvard Medical School (2005), and Brown University (2004). He participated as PI, Co-I, and Senior Personnel on projects with more than $16 million (with more than $10M as a PI) in high-performance scientific and medical image computing, and he has about 250 publications.</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=People/Nikos-Chrisochoides&diff=9292People/Nikos-Chrisochoides2024-03-06T02:18:42Z<p>Nikos: </p>
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<div>[[File:Nikos_CoS_ODU_2020.png|250px|thumb|left|frameless|CRTC Principal Investigator: Nikos Chrisochoides]]<br />
<br />
<b> Short Bio </b>: Nikos Chrisochoides is the Richard T. Cheng Endowed Chair Professor of Computer Science, an Eminent Scholar at Old Dominion University, and a Visiting Professor in the Medical School at Aristotle University of Thessaloniki (AUTh), Greece. In addition, he is a John Simon Guggenheim Fellow in Medicine & Health (2007) in the US and Canada, was elected a Nuclear Femtography Fellow in the US, and is a Distinguished Visiting Fellow in the Royal Academy of Engineering in the UK. Nikos received his Ph.D. in 1992 from Computer Science at Purdue University. From 1992 to 1997, he worked in Upstate NY, where he was the inaugural Alex Nason Fellow at Northeast Parallel Architectures Center in Syracuse and a Research Scientist at the Advanced Computing Research Institute at Cornell University. In 1997, he joined the Computer Science & Engineering Dept. at Notre Dame, where he received his NSF CAREER Award. In 2000, he joined the College of William and Mary, where he was awarded the Alumni Memorial Professorship. He has held visiting positions at MIT, Harvard Medical School, and Brown University. He participated as PI, Co-I, and Senior Personnel on projects with more than $16 million (with more than $10M as a PI) in high-performance scientific and medical image computing, and he has about 250 publications.</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=People/Nikos-Chrisochoides&diff=9291People/Nikos-Chrisochoides2024-02-02T12:23:14Z<p>Nikos: </p>
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<div>[[File:Nikos_CoS_ODU_2020.png|250px|thumb|left|frameless|CRTC Principal Investigator: Nikos Chrisochoides]]<br />
<br />
<b> Short Bio </b>: Nikos Chrisochoides is the Richard T. Cheng Endowed Chair Professor of Computer Science, an Eminent Scholar at Old Dominion University, and a Visiting Professor at the Medical School of Aristotle University of Thessaloniki (AUTh), Greece. In addition, he is a John Simon Guggenheim Fellow in Medicine & Health (2007) in the US and Canada, was elected a Nuclear Femtography Fellow in the US, and is a Distinguished Visiting Fellow in the Royal Academy of Engineering in the UK. Nikos received his Ph.D. in 1992 from Computer Science at Purdue University. From 1992 to 1997, he worked in Upstate NY, where he was the inaugural Alex Nason Fellow at Northeast Parallel Architectures Center in Syracuse and a Research Scientist at the Advanced Computing Research Institute at Cornell University. In 1997, he joined the Computer Science & Engineering Dept. at Notre Dame, where he received his NSF CAREER Award. In 2000, he joined the College of William and Mary, where he was awarded the Alumni Memorial Professorship. He has held visiting positions at MIT, Harvard Medical School, and Brown University. He participated as PI, Co-I, and Senior Personnel on projects with more than $16 million (with more than $10M as a PI) in high-performance scientific and medical image computing, and he has about 250 publications.</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=People/Nikos-Chrisochoides&diff=9290People/Nikos-Chrisochoides2024-02-02T12:12:11Z<p>Nikos: </p>
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<div>[[File:Nikos_CoS_ODU_2020.png|250px|thumb|left|frameless|CRTC Principal Investigator: Nikos Chrisochoides]]<br />
<br />
<b> Short Bio </b>: Nikos Chrisochoides is the Richard T. Cheng Endowed Chair Professor of Computer Science, an Eminent Scholar at Old Dominion University, and a Visiting Professor at the Medical School of Aristotle University of Thessaloniki (AUTh), Greece. In addition, he is a John Simon Guggenheim Fellow in Medicine & Health in the US and Canada, was elected a Nuclear Femtography Fellow in the US and is a Distinguished Visiting Fellow in the Royal Academy of Engineering in the UK. Nikos received his Ph.D. in 1992 from Computer Science at Purdue University. From 1992 to 1997, he worked in Upstate NY, where he was the inaugural Alex Nason Fellow at Northeast Parallel Architectures Center in Syracuse and a Research Scientist at the Advanced Computing Research Institute at Cornell University. In 1997, he joined the Computer Science & Engineering Dept. at Notre Dame, where he received his NSF CAREER Award. In 2000, he joined the College of William and Mary, where he was awarded the Alumni Memorial Professorship. He has held visiting positions at MIT, Harvard Medical School, and Brown University. He participated as PI, Co-I, and Senior Personnel on projects with more than $16 million (with more than $10M as a PI) in high-performance scientific and medical image computing, and he has about 250 publications.</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=People/Nikos-Chrisochoides&diff=9289People/Nikos-Chrisochoides2024-02-02T12:08:52Z<p>Nikos: </p>
<hr />
<div>[[File:Nikos_CoS_ODU_2020.png|250px|thumb|left|frameless|CRTC Principal Investigator: Nikos Chrisochoides]]<br />
<br />
<b> Short Bio </b>: Nikos Chrisochoides is the Richard T. Cheng Endowed Chair Professor of Computer Science, an Eminent Scholar at Old Dominion University, and a Visiting Professor at the Medical School of Aristotle University of Thessaloniki (AUTh), Greece. In addition, he is a John Simon Guggenheim Fellow in Medicine & Health in the US and Canada, was elected a Nuclear Femtography Fellow in the US and is a Distinguished Visiting Fellow in the Royal Academy of Engineering in the UK. Nikos received his Ph.D. in 1992 from Computer Science at Purdue University. From 1992 to 1997, he worked in Upstate NY, where he was the first Alex Nason Fellow at Northeast Parallel Architectures Center in Syracuse and a Research Scientist at the Advanced Computing Research Institute at Cornell University. In 1997, he joined the Computer Science & Engineering Dept. at Notre Dame, where he received his NSF CAREER Award. In 2000, he joined the College of William and Mary, where he was awarded the Alumni Memorial Professorship. He has held visiting positions at MIT, Harvard Medical School, and Brown University. He participated as PI, Co-I, and Senior Personnel on projects with more than $16 million (with more than $10M as a PI) in high-performance scientific and medical image computing, and he has about 250 publications.</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9288Main Page2024-01-26T01:17:46Z<p>Nikos: /* Announcements */</p>
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<div><br />
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[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
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== Announcements == <br />
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''' Professor Chrisochoides will be teaching (Fall’23) a new class on Quantum Computing; for more details, see [[https://crtc.cs.odu.edu/images/8/85/CS495-595_on_QC.png here.]]<br />
<br />
— Two students from this class got internships in Navy Research Lab and DoE’s Jefferson Lab''' <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
Nuclear Physicists confirmed these results in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshow of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
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== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
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<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
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=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9193Main Page2023-07-13T13:04:14Z<p>Nikos: /* Announcements */</p>
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<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
''' Professor Chrisochoides will be teaching (Fall’23) a new class on Quantum Computing; for more details, see [[https://crtc.cs.odu.edu/images/8/85/CS495-595_on_QC.png here.]]''' <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
Nuclear Physicists confirmed these results in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshow of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9192Main Page2023-07-13T13:02:15Z<p>Nikos: /* Announcements */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
''' Professor Chrisochoides will be teaching (Fall’23) a new class on Quantum Computing; for more details, see [[https://crtc.cs.odu.edu/File:CS495-595_on_QC.png here.]]''' <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
Nuclear Physicists confirmed these results in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshow of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=File:CS495-595_on_QC.png&diff=9191File:CS495-595 on QC.png2023-07-13T13:01:42Z<p>Nikos: Nikos uploaded a new version of File:CS495-595 on QC.png</p>
<hr />
<div></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9188Main Page2023-07-12T19:50:42Z<p>Nikos: /* Announcements */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
''' Professor Chrisochoides will be teaching (Fall’23) a new class on Quantum Computing; for more details, see [[https://crtc.cs.odu.edu/images/8/85/CS495-595_on_QC.png here.]]''' <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
Nuclear Physicists confirmed these results in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshow of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9187Main Page2023-07-12T18:03:34Z<p>Nikos: /* Announcements */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
''' Professor Chrisochoides will be teaching a new class on Quantum Computing; for more details, see [[https://crtc.cs.odu.edu/images/8/85/CS495-595_on_QC.png here.]]''' <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
Nuclear Physicists confirmed these results in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshow of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=File:CS495-595_on_QC.png&diff=9186File:CS495-595 on QC.png2023-07-12T18:01:35Z<p>Nikos: </p>
<hr />
<div></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9185Main Page2023-07-12T17:59:47Z<p>Nikos: /* Announcements */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
'''Professor Chrisochoides will be teaching a new class on Quantum Computing; for more details, see here.''' <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
Nuclear Physicists confirmed these results in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshow of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9184Main Page2023-07-12T17:59:27Z<p>Nikos: /* Announcements */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
'''Professor Chrisochoides will be teaching a new class on Quantum Computing; for more details, see here.''' <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
Nuclear Physicists confirmed these results in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshow of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9183Main Page2023-07-12T17:57:38Z<p>Nikos: /* Announcements */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
* Professor Chrisochoides will be teaching a new class on Quantum Computing; for more details, see here. <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshows of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9182Main Page2023-07-12T17:57:14Z<p>Nikos: /* Announcements */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
Professor Chrisochoides will be teaching a new class on Quantum Computing; for more details, see here. <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshows of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9181Main Page2023-07-10T10:56:08Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshows of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9180Main Page2023-07-08T10:25:32Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.''' <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshows of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Resources&diff=9152Resources2023-07-02T20:08:42Z<p>Nikos: </p>
<hr />
<div>[[SLURM_usage_summary]]<br />
<br />
[[Internal:Resources]]<br />
<br />
[[External:Resources]]<br />
<br />
https://www.alcf.anl.gov/presentations<br />
<br />
[[External:Quantum Computing]]</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9086Main Page2023-05-20T13:55:18Z<p>Nikos: /* Introduction */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshows of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9085Main Page2023-05-20T11:03:08Z<p>Nikos: /* Introduction */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshows of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/patent/WO2014032011A3/en Six patent applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9084Main Page2023-05-20T11:00:03Z<p>Nikos: /* Introduction */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshows of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/patent/WO2014032011A3/en Six patents applications were filled.]<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9082Main Page2023-05-18T22:04:44Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshows of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=People&diff=9071People2023-05-17T12:48:19Z<p>Nikos: /* Former PhD Students */</p>
<hr />
<div><br />
== Current Graduate Students ==<br />
<br />
{| class="wikitable"<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:REU_KG.jpg|108px]] <br />
|<b>Kevin Garner</b><br> Graduate Research Assistant<br />
<br />
| Scalable Adaptive Anisotropic Local Reconnection Mesh Generation Method, Machine Learning<br />
<br />
|-<br />
|style="background: white"|[[Image:Emmanuel.jpeg|108px]] <br />
|<b> Emmanuel Billias </b><br> Graduate Research Assistant<br />
<br />
| Quantum Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Unknown.jpg|108px]] <br />
|<b>Chris Rector</b><br> Graduate Research Assistant<br />
<br />
| Quantum Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Unknown.jpg|108px]] <br />
|<b>Devon Underhill</b><br> Graduate Research Assistant<br />
<br />
| Quantum Computing<br />
<br />
<br />
|}<br />
<br />
== Principal Investigator ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Nikos_CoS_ODU_2020.png|108px]]<br />
|[https://crtc.cs.odu.edu/index.php/People/Nikos-Chrisochoides <b>Dr. Nikos Chrisochoides</b>]<br />
<br />
|Quantum Computing & Exascale Computing, <br />
<br />
Parallel Mesh Generation and Runtime Software Systems,<br />
<br />
Medical Image Computing. <br />
<br />
|}<br />
<br />
== Former PhD Students ==<br />
<br />
{| class="wikitable"<br />
|-<br />
|style="background: white"|[[Image:Student_Thomadakis.jpg|108px]]<br />
|<b>Polykarpos Thomadakis</b><br> Graduate Research Assistant, ODU<br />
<br />
| Parallel Runtime Systems, Machine Learning<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Tsolakis.jpg|108px]]<br />
|[http://www.cs.odu.edu/~ctsolakis <b>Christos Tsolakis</b>]<br> Graduate Research Assistant, ODU<br />
<br />
| Parallel Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Drakopoulos.jpg|108px]]<br />
|<b>Fotis Drakopoulos</b> <br> Graduate Research Assistant, ODU<br />
<br />
| Parallel Mesh Generation and Medical Image Computing<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Foteinos.jpg|108px]] <br />
|<b>Panagiotis Foteinos</b><br> Research Assistant, W&M/ODU<br />
<br />
| Parallel Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Liu.jpg|108px]]<br />
|<b>Yixun Liu</b><br> Research Assistant, W&M/ODU<br />
<br />
| Medical Image Computing<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Collaborator_Kot.jpg|108px]]<br />
| [http://web-test.ncsa.illinois.edu/AboutUs/People/contact.php?id=kot <b>Dr. Andriy Kot</b>] Research Assistant, W&M/ODU<br />
<br />
| Parallel Runtime Software Systems<br />
<br />
|-<br />
|style="background: white"|[[Image:Fedorov.jpg|108px]] <br />
|<b>Andriy Fedorov</b><br> Research Assistant, W&M<br />
<br />
| Medical Image Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Linardakis.jpg|108px]] <br />
|<b>Leonidas Linardakis</b><br> Research Assistant, W&M<br />
<br />
| Parallel Mesh Generation<br />
<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Chernikov.jpg|108px]]<br />
|<b>Andrey Chernikov</b><br> Research Assistant, W&M <br />
<br />
| Parallel Mesh Generation <br />
<br />
|-<br />
|style="background: white"|[[Image:Barker.jpg|108px]] <br />
|<b>Kevin Barker</b><br> Research Assistant, W&M<br />
<br />
| Parallel Runtime Software Systems<br />
<br />
|}<br />
<br />
== -- Postdocs == <br />
<br />
{| class="wikitable"<br />
<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Giannakos.jpg|108px]]<br />
|<b>Michail Giannakos</b><br> Professor, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Collaborator_Kot.jpg|108px]]<br />
| [http://web-test.ncsa.illinois.edu/AboutUs/People/contact.php?id=kot <b>Dr. Andriy Kot</b>], ODU<br />
<br />
| Parallel Runtime Software Systems<br />
<br />
|-<br />
|style="background: white"|[[Image:Liu.jpg|108px]]<br />
|<b>Yixun Liu</b><br> Adjunct Professor, ODU<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Chernikov.jpg|108px]]<br />
|<b>Andrey Chernikov</b><br> Associate Professor, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Antonopoulos.jpg|108px]] <br />
|<b>Christos Antonopoulos</b><br> Adjunct Senior Research Associate, W&M<br />
<br />
|<br />
<br />
|}<br />
<br />
== -- MSc Students ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Joi.JPG|108px]] <br />
|<b>Joi Best</b><br> Graduate Research Assistant, ODU<br />
<br />
| Image to Mesh Conversion<br />
<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Almahallawy.jpg|108px]] <br />
|<b>Khaled Almahallawy</b><br> Research Assistant, ODU<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:McKnight.jpg|108px]] <br />
|<b>Andrew McKnight</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Kenzor.png|108px]] <br />
|<b>Denis Kenzor</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Billet.jpg|108px]] <br />
|<b>Eric Billet</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Zagaris.jpg|108px]] <br />
|<b>George Zagaris</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Weissberger.jpg|108px]] <br />
|<b>Michael Weissberger</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Verma.jpg|108px]] <br />
|<b>Chaman Verma</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Holinka.jpg|108px]] <br />
|<b>Brian Holinka</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Nave.jpg|108px]] <br />
|<b>Demian Nave</b><br> Research Assistant, Notre Dame<br />
<br />
|<br />
<br />
|}<br />
<br />
== -- REU and High School Students ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Joi.JPG|108px]] <br />
|<b>Joi Best</b><br> Graduate Research Assistant, ODU<br />
<br />
| Image to Mesh Conversion<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Angelopoulos.png|108px]]<br />
|[https://www.linkedin.com/in/angelos-angelopoulos-038701162/ <b>Angelos Angelopoulos</b>]<br> Research Assistant, ODU<br />
<br />
| Software Engineering, Machine Learning, High-Performance Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:REU_Alexis.jpg|108px]] <br />
|<b>Alexis Brueggeman</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:MirandaSmith.png|108px]] <br />
|<b>Miranda Smith</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Smith.jpg|108px]] <br />
|<b>Alison Smith</b><br> Undergraduate Research Assistant, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Folsom.jpg|108px]] <br />
|<b>Sydney Folsom</b><br> Visiting High School Research Assistant, ODU<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Hines.png|108px]] <br />
|<b>Matt Hines</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Trotta.jpg|108px]] <br />
|<b>Michael Trotta</b><br> REU Student, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Williams.jpg|108px]] <br />
|<b>Alex Williams</b><br> Visiting High School Research Assistant, W&M<br />
<br />
|<br />
<br />
|}<br />
<br />
== Former Faculty & Staff ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Chernikov.jpg|108px]]<br />
|<b>Andrey Chernikov</b><br> Associate Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Nadeem.jpg|108px]] <br />
|<b>Tamer Nadeem</b><br> Associate Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:ChungHaoChen.png|108px]]<br />
|<b>Chung Hao Chen</b><br> Assistant Professor<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:ThomasKennedy.png|108px]] <br />
|[http://odu.edu/directory/people/t/tkenn013 <b>Thomas Kennedy</b>]<br> Instructor<br />
<br />
| High-Performance Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Sun.jpg|108px]]<br />
|<b>Jiangwen Sun</b><br> Assistant Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Li.jpg|108px]]<br />
|<b>Weidong Li</b><br> Visiting Assistant Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Ji.jpg|108px]] <br />
|<b>Shuiwang Ji</b><br> Assistant Professor<br />
<br />
|<br />
<br />
<br />
<br />
|}<br />
<br />
<br />
== External Collaborators ==<br />
<br />
{| class="wikitable"<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Visitor_Nikolopoulos.jpg|108px]]<br />
| [http://www.cs.qub.ac.uk/~D.Nikolopoulos/ <b>Prof. Dimitris Nikolopoulos</b>]<br />
<br />
| Parallel Power Aware Runtime Systems <br />
<br />
Programming Languages<br />
<br />
|}<br />
<br />
== Visitors ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Darmofal_David.jpg|108px]]<br />
|[https://darmofal.mit.edu/ <b>David Darmofal</b>]<br />
<br />
| Parallel Error-based metrics<br />
<br />
Parallel Adaptive Mesh Refinement<br />
<br />
|-<br />
|style="background: white"|[[Image:Marcum_David.jpg|108px]]<br />
| [http://www.me.msstate.edu/faculty/marcum/marcum.html <b>David Marcum</b>]<br />
<br />
| Parallel Anisotropic AFT-based Mesh Generation and Adaption <br />
<br />
Interactions with Geometry<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Collaborator_Jiao.jpg|108px]]<br />
| [http://www.ams.sunysb.edu/~jiao/ <b>Prof. Jim Jiao</b>]<br />
<br />
| Scalable Abstractions for Geometry<br />
<br />
|-<br />
|style="background: white"|[[Image:Antonopoulos.jpg|108px]] <br />
|<b>Christos Antonopoulos</b><br> Adjunct Senior Research Associate<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Schneipp.jpg|108px]] <br />
|<b>Hannes Schneipp</b><br> Assistant Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Zhang.jpg|108px]] <br />
|<b>Jessica Zhang</b><br> Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Lesnick.jpg|108px]] <br />
|<b>Jim Lesnick</b><br> RMG Neurosurgeon<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Han.jpg|108px]] <br />
|<b>Joseph Han</b><br> Associate Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Behr.jpg|108px]] <br />
|<b>Marek Behr</b><br> Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Clatz.jpg|108px]] <br />
|<b>Olivier Clatz</b><br> Research Fellow<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Shontz.jpg|108px]] <br />
|<b>Suzanne M. Shontz</b><br> Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Papademitris.jpg|108px]] <br />
|<b>Xenios Papademitris</b><br> Assistant Professor<br />
<br />
|<br />
<br />
|}<br />
<br />
== Alumni Graduate Students ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Adam.jpg|108px]] <br />
|<b>Eleni Adam</b><br> Graduate Research Assistant<br />
<br />
| Medical Image Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Kyriaki_Kavazidi.jpg|108px]]<br />
|<b>Kyriaki Rafailia Kavazidi</b><br />
<br />
| Medical Student<br />
<br />
|-<br />
|style="background: white"| [[Image:Student_Spiros_Tsalikis.jpg|109px]]<br />
|[https://www.linkedin.com/in/Spiros-Tsalikis/ <b>Spiros Tsalikis</b>]<br> Graduate Research Assistant, ODU<br />
<br />
| High-Performance Computing<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Feng.jpg|108px]]<br />
|<b>Daming Feng</b><br> Graduate Research Assistant<br />
<br />
| Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Xu.jpg|108px]] <br />
|<b>Jing Xu</b><br> Research Assistant<br />
<br />
| Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Ahmed.png|108px]] <br />
|<b>Ahmed Fakhry</b><br> Research Assistant<br />
<br />
| Machine Learning<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Karthik.jpg|108px]] <br />
|<b>Karthik Navuluri</b><br> PhD Student<br />
<br />
| Machine Learning<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Rongjian.png|108px]] <br />
|<b>Rongjian Li</b><br> Research Assistant<br />
<br />
| Machine Learning<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Zeng.png|108px]] <br />
|<b>Tao Zeng</b><br> Research Assistant<br />
<br />
| Machine Learning<br />
<br />
|-<br />
|style="background: white"|[[Image:WenluZhang.jpg|108px]] <br />
|<b>Wenlu Zhang</b><br> PhD Student<br />
<br />
| Machine Learning<br />
<br />
|}</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=People&diff=9070People2023-05-17T12:46:49Z<p>Nikos: /* Former PhD Students */</p>
<hr />
<div><br />
== Current Graduate Students ==<br />
<br />
{| class="wikitable"<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:REU_KG.jpg|108px]] <br />
|<b>Kevin Garner</b><br> Graduate Research Assistant<br />
<br />
| Scalable Adaptive Anisotropic Local Reconnection Mesh Generation Method, Machine Learning<br />
<br />
|-<br />
|style="background: white"|[[Image:Emmanuel.jpeg|108px]] <br />
|<b> Emmanuel Billias </b><br> Graduate Research Assistant<br />
<br />
| Quantum Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Unknown.jpg|108px]] <br />
|<b>Chris Rector</b><br> Graduate Research Assistant<br />
<br />
| Quantum Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Unknown.jpg|108px]] <br />
|<b>Devon Underhill</b><br> Graduate Research Assistant<br />
<br />
| Quantum Computing<br />
<br />
<br />
|}<br />
<br />
== Principal Investigator ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Nikos_CoS_ODU_2020.png|108px]]<br />
|[https://crtc.cs.odu.edu/index.php/People/Nikos-Chrisochoides <b>Dr. Nikos Chrisochoides</b>]<br />
<br />
|Quantum Computing & Exascale Computing, <br />
<br />
Parallel Mesh Generation and Runtime Software Systems,<br />
<br />
Medical Image Computing. <br />
<br />
|}<br />
<br />
== Former PhD Students ==<br />
<br />
{| class="wikitable"<br />
|-<br />
|style="background: white"|[[Image:Student_Thomadakis.jpg|108px]]<br />
|<b>Polykarpos Thomadakis</b><br> Graduate Research Assistant<br />
<br />
| Parallel Runtime Systems, Machine Learning<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Tsolakis.jpg|108px]]<br />
|[http://www.cs.odu.edu/~ctsolakis <b>Christos Tsolakis</b>]<br> Graduate Research Assistant, ODU<br />
<br />
| Parallel Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Drakopoulos.jpg|108px]]<br />
|<b>Fotis Drakopoulos</b> <br> Graduate Research Assistant, ODU<br />
<br />
| Parallel Mesh Generation and Medical Image Computing<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Foteinos.jpg|108px]] <br />
|<b>Panagiotis Foteinos</b><br> Research Assistant, W&M/ODU<br />
<br />
| Parallel Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Liu.jpg|108px]]<br />
|<b>Yixun Liu</b><br> Research Assistant, W&M/ODU<br />
<br />
| Medical Image Computing<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Collaborator_Kot.jpg|108px]]<br />
| [http://web-test.ncsa.illinois.edu/AboutUs/People/contact.php?id=kot <b>Dr. Andriy Kot</b>] Research Assistant, W&M/ODU<br />
<br />
| Parallel Runtime Software Systems<br />
<br />
|-<br />
|style="background: white"|[[Image:Fedorov.jpg|108px]] <br />
|<b>Andriy Fedorov</b><br> Research Assistant, W&M<br />
<br />
| Medical Image Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Linardakis.jpg|108px]] <br />
|<b>Leonidas Linardakis</b><br> Research Assistant, W&M<br />
<br />
| Parallel Mesh Generation<br />
<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Chernikov.jpg|108px]]<br />
|<b>Andrey Chernikov</b><br> Research Assistant, W&M <br />
<br />
| Parallel Mesh Generation <br />
<br />
|-<br />
|style="background: white"|[[Image:Barker.jpg|108px]] <br />
|<b>Kevin Barker</b><br> Research Assistant, W&M<br />
<br />
| Parallel Runtime Software Systems<br />
<br />
|}<br />
<br />
== -- Postdocs == <br />
<br />
{| class="wikitable"<br />
<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Giannakos.jpg|108px]]<br />
|<b>Michail Giannakos</b><br> Professor, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Collaborator_Kot.jpg|108px]]<br />
| [http://web-test.ncsa.illinois.edu/AboutUs/People/contact.php?id=kot <b>Dr. Andriy Kot</b>], ODU<br />
<br />
| Parallel Runtime Software Systems<br />
<br />
|-<br />
|style="background: white"|[[Image:Liu.jpg|108px]]<br />
|<b>Yixun Liu</b><br> Adjunct Professor, ODU<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Chernikov.jpg|108px]]<br />
|<b>Andrey Chernikov</b><br> Associate Professor, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Antonopoulos.jpg|108px]] <br />
|<b>Christos Antonopoulos</b><br> Adjunct Senior Research Associate, W&M<br />
<br />
|<br />
<br />
|}<br />
<br />
== -- MSc Students ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Joi.JPG|108px]] <br />
|<b>Joi Best</b><br> Graduate Research Assistant, ODU<br />
<br />
| Image to Mesh Conversion<br />
<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Almahallawy.jpg|108px]] <br />
|<b>Khaled Almahallawy</b><br> Research Assistant, ODU<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:McKnight.jpg|108px]] <br />
|<b>Andrew McKnight</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Kenzor.png|108px]] <br />
|<b>Denis Kenzor</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Billet.jpg|108px]] <br />
|<b>Eric Billet</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Zagaris.jpg|108px]] <br />
|<b>George Zagaris</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Weissberger.jpg|108px]] <br />
|<b>Michael Weissberger</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Verma.jpg|108px]] <br />
|<b>Chaman Verma</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Holinka.jpg|108px]] <br />
|<b>Brian Holinka</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Nave.jpg|108px]] <br />
|<b>Demian Nave</b><br> Research Assistant, Notre Dame<br />
<br />
|<br />
<br />
|}<br />
<br />
== -- REU and High School Students ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Joi.JPG|108px]] <br />
|<b>Joi Best</b><br> Graduate Research Assistant, ODU<br />
<br />
| Image to Mesh Conversion<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Angelopoulos.png|108px]]<br />
|[https://www.linkedin.com/in/angelos-angelopoulos-038701162/ <b>Angelos Angelopoulos</b>]<br> Research Assistant, ODU<br />
<br />
| Software Engineering, Machine Learning, High-Performance Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:REU_Alexis.jpg|108px]] <br />
|<b>Alexis Brueggeman</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:MirandaSmith.png|108px]] <br />
|<b>Miranda Smith</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Smith.jpg|108px]] <br />
|<b>Alison Smith</b><br> Undergraduate Research Assistant, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Folsom.jpg|108px]] <br />
|<b>Sydney Folsom</b><br> Visiting High School Research Assistant, ODU<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Hines.png|108px]] <br />
|<b>Matt Hines</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Trotta.jpg|108px]] <br />
|<b>Michael Trotta</b><br> REU Student, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Williams.jpg|108px]] <br />
|<b>Alex Williams</b><br> Visiting High School Research Assistant, W&M<br />
<br />
|<br />
<br />
|}<br />
<br />
== Former Faculty & Staff ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Chernikov.jpg|108px]]<br />
|<b>Andrey Chernikov</b><br> Associate Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Nadeem.jpg|108px]] <br />
|<b>Tamer Nadeem</b><br> Associate Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:ChungHaoChen.png|108px]]<br />
|<b>Chung Hao Chen</b><br> Assistant Professor<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:ThomasKennedy.png|108px]] <br />
|[http://odu.edu/directory/people/t/tkenn013 <b>Thomas Kennedy</b>]<br> Instructor<br />
<br />
| High-Performance Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Sun.jpg|108px]]<br />
|<b>Jiangwen Sun</b><br> Assistant Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Li.jpg|108px]]<br />
|<b>Weidong Li</b><br> Visiting Assistant Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Ji.jpg|108px]] <br />
|<b>Shuiwang Ji</b><br> Assistant Professor<br />
<br />
|<br />
<br />
<br />
<br />
|}<br />
<br />
<br />
== External Collaborators ==<br />
<br />
{| class="wikitable"<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Visitor_Nikolopoulos.jpg|108px]]<br />
| [http://www.cs.qub.ac.uk/~D.Nikolopoulos/ <b>Prof. Dimitris Nikolopoulos</b>]<br />
<br />
| Parallel Power Aware Runtime Systems <br />
<br />
Programming Languages<br />
<br />
|}<br />
<br />
== Visitors ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Darmofal_David.jpg|108px]]<br />
|[https://darmofal.mit.edu/ <b>David Darmofal</b>]<br />
<br />
| Parallel Error-based metrics<br />
<br />
Parallel Adaptive Mesh Refinement<br />
<br />
|-<br />
|style="background: white"|[[Image:Marcum_David.jpg|108px]]<br />
| [http://www.me.msstate.edu/faculty/marcum/marcum.html <b>David Marcum</b>]<br />
<br />
| Parallel Anisotropic AFT-based Mesh Generation and Adaption <br />
<br />
Interactions with Geometry<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Collaborator_Jiao.jpg|108px]]<br />
| [http://www.ams.sunysb.edu/~jiao/ <b>Prof. Jim Jiao</b>]<br />
<br />
| Scalable Abstractions for Geometry<br />
<br />
|-<br />
|style="background: white"|[[Image:Antonopoulos.jpg|108px]] <br />
|<b>Christos Antonopoulos</b><br> Adjunct Senior Research Associate<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Schneipp.jpg|108px]] <br />
|<b>Hannes Schneipp</b><br> Assistant Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Zhang.jpg|108px]] <br />
|<b>Jessica Zhang</b><br> Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Lesnick.jpg|108px]] <br />
|<b>Jim Lesnick</b><br> RMG Neurosurgeon<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Han.jpg|108px]] <br />
|<b>Joseph Han</b><br> Associate Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Behr.jpg|108px]] <br />
|<b>Marek Behr</b><br> Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Clatz.jpg|108px]] <br />
|<b>Olivier Clatz</b><br> Research Fellow<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Shontz.jpg|108px]] <br />
|<b>Suzanne M. Shontz</b><br> Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Papademitris.jpg|108px]] <br />
|<b>Xenios Papademitris</b><br> Assistant Professor<br />
<br />
|<br />
<br />
|}<br />
<br />
== Alumni Graduate Students ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Adam.jpg|108px]] <br />
|<b>Eleni Adam</b><br> Graduate Research Assistant<br />
<br />
| Medical Image Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Kyriaki_Kavazidi.jpg|108px]]<br />
|<b>Kyriaki Rafailia Kavazidi</b><br />
<br />
| Medical Student<br />
<br />
|-<br />
|style="background: white"| [[Image:Student_Spiros_Tsalikis.jpg|109px]]<br />
|[https://www.linkedin.com/in/Spiros-Tsalikis/ <b>Spiros Tsalikis</b>]<br> Graduate Research Assistant, ODU<br />
<br />
| High-Performance Computing<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Feng.jpg|108px]]<br />
|<b>Daming Feng</b><br> Graduate Research Assistant<br />
<br />
| Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Xu.jpg|108px]] <br />
|<b>Jing Xu</b><br> Research Assistant<br />
<br />
| Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Ahmed.png|108px]] <br />
|<b>Ahmed Fakhry</b><br> Research Assistant<br />
<br />
| Machine Learning<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Karthik.jpg|108px]] <br />
|<b>Karthik Navuluri</b><br> PhD Student<br />
<br />
| Machine Learning<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Rongjian.png|108px]] <br />
|<b>Rongjian Li</b><br> Research Assistant<br />
<br />
| Machine Learning<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Zeng.png|108px]] <br />
|<b>Tao Zeng</b><br> Research Assistant<br />
<br />
| Machine Learning<br />
<br />
|-<br />
|style="background: white"|[[Image:WenluZhang.jpg|108px]] <br />
|<b>Wenlu Zhang</b><br> PhD Student<br />
<br />
| Machine Learning<br />
<br />
|}</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=People&diff=9069People2023-05-17T12:46:24Z<p>Nikos: /* Current Graduate Students */</p>
<hr />
<div><br />
== Current Graduate Students ==<br />
<br />
{| class="wikitable"<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:REU_KG.jpg|108px]] <br />
|<b>Kevin Garner</b><br> Graduate Research Assistant<br />
<br />
| Scalable Adaptive Anisotropic Local Reconnection Mesh Generation Method, Machine Learning<br />
<br />
|-<br />
|style="background: white"|[[Image:Emmanuel.jpeg|108px]] <br />
|<b> Emmanuel Billias </b><br> Graduate Research Assistant<br />
<br />
| Quantum Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Unknown.jpg|108px]] <br />
|<b>Chris Rector</b><br> Graduate Research Assistant<br />
<br />
| Quantum Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Unknown.jpg|108px]] <br />
|<b>Devon Underhill</b><br> Graduate Research Assistant<br />
<br />
| Quantum Computing<br />
<br />
<br />
|}<br />
<br />
== Principal Investigator ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Nikos_CoS_ODU_2020.png|108px]]<br />
|[https://crtc.cs.odu.edu/index.php/People/Nikos-Chrisochoides <b>Dr. Nikos Chrisochoides</b>]<br />
<br />
|Quantum Computing & Exascale Computing, <br />
<br />
Parallel Mesh Generation and Runtime Software Systems,<br />
<br />
Medical Image Computing. <br />
<br />
|}<br />
<br />
== Former PhD Students ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Tsolakis.jpg|108px]]<br />
|[http://www.cs.odu.edu/~ctsolakis <b>Christos Tsolakis</b>]<br> Graduate Research Assistant, ODU<br />
<br />
| Parallel Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Drakopoulos.jpg|108px]]<br />
|<b>Fotis Drakopoulos</b> <br> Graduate Research Assistant, ODU<br />
<br />
| Parallel Mesh Generation and Medical Image Computing<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Foteinos.jpg|108px]] <br />
|<b>Panagiotis Foteinos</b><br> Research Assistant, W&M/ODU<br />
<br />
| Parallel Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Liu.jpg|108px]]<br />
|<b>Yixun Liu</b><br> Research Assistant, W&M/ODU<br />
<br />
| Medical Image Computing<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Collaborator_Kot.jpg|108px]]<br />
| [http://web-test.ncsa.illinois.edu/AboutUs/People/contact.php?id=kot <b>Dr. Andriy Kot</b>] Research Assistant, W&M/ODU<br />
<br />
| Parallel Runtime Software Systems<br />
<br />
|-<br />
|style="background: white"|[[Image:Fedorov.jpg|108px]] <br />
|<b>Andriy Fedorov</b><br> Research Assistant, W&M<br />
<br />
| Medical Image Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Linardakis.jpg|108px]] <br />
|<b>Leonidas Linardakis</b><br> Research Assistant, W&M<br />
<br />
| Parallel Mesh Generation<br />
<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Chernikov.jpg|108px]]<br />
|<b>Andrey Chernikov</b><br> Research Assistant, W&M <br />
<br />
| Parallel Mesh Generation <br />
<br />
|-<br />
|style="background: white"|[[Image:Barker.jpg|108px]] <br />
|<b>Kevin Barker</b><br> Research Assistant, W&M<br />
<br />
| Parallel Runtime Software Systems<br />
<br />
|}<br />
<br />
== -- Postdocs == <br />
<br />
{| class="wikitable"<br />
<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Giannakos.jpg|108px]]<br />
|<b>Michail Giannakos</b><br> Professor, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Collaborator_Kot.jpg|108px]]<br />
| [http://web-test.ncsa.illinois.edu/AboutUs/People/contact.php?id=kot <b>Dr. Andriy Kot</b>], ODU<br />
<br />
| Parallel Runtime Software Systems<br />
<br />
|-<br />
|style="background: white"|[[Image:Liu.jpg|108px]]<br />
|<b>Yixun Liu</b><br> Adjunct Professor, ODU<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Chernikov.jpg|108px]]<br />
|<b>Andrey Chernikov</b><br> Associate Professor, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Antonopoulos.jpg|108px]] <br />
|<b>Christos Antonopoulos</b><br> Adjunct Senior Research Associate, W&M<br />
<br />
|<br />
<br />
|}<br />
<br />
== -- MSc Students ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Joi.JPG|108px]] <br />
|<b>Joi Best</b><br> Graduate Research Assistant, ODU<br />
<br />
| Image to Mesh Conversion<br />
<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Almahallawy.jpg|108px]] <br />
|<b>Khaled Almahallawy</b><br> Research Assistant, ODU<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:McKnight.jpg|108px]] <br />
|<b>Andrew McKnight</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Kenzor.png|108px]] <br />
|<b>Denis Kenzor</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Billet.jpg|108px]] <br />
|<b>Eric Billet</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Zagaris.jpg|108px]] <br />
|<b>George Zagaris</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Weissberger.jpg|108px]] <br />
|<b>Michael Weissberger</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Verma.jpg|108px]] <br />
|<b>Chaman Verma</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Holinka.jpg|108px]] <br />
|<b>Brian Holinka</b><br> Research Assistant, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Nave.jpg|108px]] <br />
|<b>Demian Nave</b><br> Research Assistant, Notre Dame<br />
<br />
|<br />
<br />
|}<br />
<br />
== -- REU and High School Students ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Joi.JPG|108px]] <br />
|<b>Joi Best</b><br> Graduate Research Assistant, ODU<br />
<br />
| Image to Mesh Conversion<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Angelopoulos.png|108px]]<br />
|[https://www.linkedin.com/in/angelos-angelopoulos-038701162/ <b>Angelos Angelopoulos</b>]<br> Research Assistant, ODU<br />
<br />
| Software Engineering, Machine Learning, High-Performance Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:REU_Alexis.jpg|108px]] <br />
|<b>Alexis Brueggeman</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:MirandaSmith.png|108px]] <br />
|<b>Miranda Smith</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Smith.jpg|108px]] <br />
|<b>Alison Smith</b><br> Undergraduate Research Assistant, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Folsom.jpg|108px]] <br />
|<b>Sydney Folsom</b><br> Visiting High School Research Assistant, ODU<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Hines.png|108px]] <br />
|<b>Matt Hines</b><br> REU Student, ODU<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Trotta.jpg|108px]] <br />
|<b>Michael Trotta</b><br> REU Student, W&M<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Williams.jpg|108px]] <br />
|<b>Alex Williams</b><br> Visiting High School Research Assistant, W&M<br />
<br />
|<br />
<br />
|}<br />
<br />
== Former Faculty & Staff ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Chernikov.jpg|108px]]<br />
|<b>Andrey Chernikov</b><br> Associate Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Nadeem.jpg|108px]] <br />
|<b>Tamer Nadeem</b><br> Associate Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:ChungHaoChen.png|108px]]<br />
|<b>Chung Hao Chen</b><br> Assistant Professor<br />
<br />
|<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:ThomasKennedy.png|108px]] <br />
|[http://odu.edu/directory/people/t/tkenn013 <b>Thomas Kennedy</b>]<br> Instructor<br />
<br />
| High-Performance Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Sun.jpg|108px]]<br />
|<b>Jiangwen Sun</b><br> Assistant Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Li.jpg|108px]]<br />
|<b>Weidong Li</b><br> Visiting Assistant Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Ji.jpg|108px]] <br />
|<b>Shuiwang Ji</b><br> Assistant Professor<br />
<br />
|<br />
<br />
<br />
<br />
|}<br />
<br />
<br />
== External Collaborators ==<br />
<br />
{| class="wikitable"<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Visitor_Nikolopoulos.jpg|108px]]<br />
| [http://www.cs.qub.ac.uk/~D.Nikolopoulos/ <b>Prof. Dimitris Nikolopoulos</b>]<br />
<br />
| Parallel Power Aware Runtime Systems <br />
<br />
Programming Languages<br />
<br />
|}<br />
<br />
== Visitors ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Darmofal_David.jpg|108px]]<br />
|[https://darmofal.mit.edu/ <b>David Darmofal</b>]<br />
<br />
| Parallel Error-based metrics<br />
<br />
Parallel Adaptive Mesh Refinement<br />
<br />
|-<br />
|style="background: white"|[[Image:Marcum_David.jpg|108px]]<br />
| [http://www.me.msstate.edu/faculty/marcum/marcum.html <b>David Marcum</b>]<br />
<br />
| Parallel Anisotropic AFT-based Mesh Generation and Adaption <br />
<br />
Interactions with Geometry<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Collaborator_Jiao.jpg|108px]]<br />
| [http://www.ams.sunysb.edu/~jiao/ <b>Prof. Jim Jiao</b>]<br />
<br />
| Scalable Abstractions for Geometry<br />
<br />
|-<br />
|style="background: white"|[[Image:Antonopoulos.jpg|108px]] <br />
|<b>Christos Antonopoulos</b><br> Adjunct Senior Research Associate<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Schneipp.jpg|108px]] <br />
|<b>Hannes Schneipp</b><br> Assistant Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Zhang.jpg|108px]] <br />
|<b>Jessica Zhang</b><br> Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Lesnick.jpg|108px]] <br />
|<b>Jim Lesnick</b><br> RMG Neurosurgeon<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Han.jpg|108px]] <br />
|<b>Joseph Han</b><br> Associate Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Behr.jpg|108px]] <br />
|<b>Marek Behr</b><br> Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Clatz.jpg|108px]] <br />
|<b>Olivier Clatz</b><br> Research Fellow<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Shontz.jpg|108px]] <br />
|<b>Suzanne M. Shontz</b><br> Professor<br />
<br />
|<br />
<br />
|-<br />
|style="background: white"|[[Image:Papademitris.jpg|108px]] <br />
|<b>Xenios Papademitris</b><br> Assistant Professor<br />
<br />
|<br />
<br />
|}<br />
<br />
== Alumni Graduate Students ==<br />
<br />
{| class="wikitable"<br />
<br />
|-<br />
|style="background: white"|[[Image:Adam.jpg|108px]] <br />
|<b>Eleni Adam</b><br> Graduate Research Assistant<br />
<br />
| Medical Image Computing<br />
<br />
|-<br />
|style="background: white"|[[Image:Student_Kyriaki_Kavazidi.jpg|108px]]<br />
|<b>Kyriaki Rafailia Kavazidi</b><br />
<br />
| Medical Student<br />
<br />
|-<br />
|style="background: white"| [[Image:Student_Spiros_Tsalikis.jpg|109px]]<br />
|[https://www.linkedin.com/in/Spiros-Tsalikis/ <b>Spiros Tsalikis</b>]<br> Graduate Research Assistant, ODU<br />
<br />
| High-Performance Computing<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Feng.jpg|108px]]<br />
|<b>Daming Feng</b><br> Graduate Research Assistant<br />
<br />
| Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Xu.jpg|108px]] <br />
|<b>Jing Xu</b><br> Research Assistant<br />
<br />
| Mesh Generation<br />
<br />
|-<br />
|style="background: white"|[[Image:Ahmed.png|108px]] <br />
|<b>Ahmed Fakhry</b><br> Research Assistant<br />
<br />
| Machine Learning<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Karthik.jpg|108px]] <br />
|<b>Karthik Navuluri</b><br> PhD Student<br />
<br />
| Machine Learning<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Rongjian.png|108px]] <br />
|<b>Rongjian Li</b><br> Research Assistant<br />
<br />
| Machine Learning<br />
<br />
<br />
|-<br />
|style="background: white"|[[Image:Zeng.png|108px]] <br />
|<b>Tao Zeng</b><br> Research Assistant<br />
<br />
| Machine Learning<br />
<br />
|-<br />
|style="background: white"|[[Image:WenluZhang.jpg|108px]] <br />
|<b>Wenlu Zhang</b><br> PhD Student<br />
<br />
| Machine Learning<br />
<br />
|}</div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9061Main Page2023-05-13T21:43:59Z<p>Nikos: /* Protein-to-Protein Interactions */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article (citation) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] )of the data in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
A slideshows of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9060Main Page2023-05-13T21:41:28Z<p>Nikos: /* Announcements */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article (citation) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] )of the data in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Check our work in progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
Molecular Geometric Feature Analysis for SARS-CoV-2: to see slideshows click [[Molecular Geometric Feature Analysis | here]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9059Main Page2023-05-13T21:35:49Z<p>Nikos: /* Announcements */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article (citation) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] )of the data in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== Protein-to-Protein Interactions ===<br />
Check our work in progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9058Main Page2023-05-13T21:32:05Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged in a recent article (citation) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] )of the data in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9057Main Page2023-05-13T21:26:53Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] )of the data in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9056Main Page2023-05-13T21:22:24Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the denoising work in the workflow.<br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9049Main Page2023-05-12T20:07:48Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables of approximately 50-80%. <br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9046Main Page2023-05-12T18:13:13Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables of approximately 50-80%. <br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning, using both this classical parallel intelligent runtime system. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9045Main Page2023-05-12T17:42:05Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for Domain-Specific Language that transparently help to maintain correctness, and hide the idiosyncrasies of low-level hardware from modern exascale-era heterogeneous architectures.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables of approximately 50-80%. <br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning, using both this classical parallel intelligent runtime system. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9044Main Page2023-05-12T17:39:55Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for Domain-Specific Language that transparently help to maintain correctness, and hide the idiosyncrasies of low-level hardware from modern exascale-era heterogeneous architectures.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables of approximately 50-80%. <br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning, using both his current runtime system and AI work. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9043Main Page2023-05-12T17:37:12Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for Domain-Specific Language that transparently help to maintain correctness, and hide the idiosyncrasies of low-level hardware from modern exascale-era heterogeneous architectures.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables of approximately 50-80%. <br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work in “steroids” using Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9042Main Page2023-05-12T15:31:27Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for Domain-Specific Language (iDSL) that transparently help to maintain correctness, hides the idiosyncrasies of low-level hardware.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables of approximately 50-80%. <br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work in “steroids” using Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9041Main Page2023-05-12T15:02:05Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work towards intelligent Domain-Specific Language (iDSL) that transparently help to maintain correctness, hides the idiosyncrasies of low-level hardware.<br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables of approximately 50-80%. <br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work in “steroids” using Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9040Main Page2023-05-12T14:17:37Z<p>Nikos: /* Congratulations to Dr. Thomadakis */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
Congratulations to Dr. Thomadakis for his impressive Ph.D. work and for defending and officially completing all his Ph.D. requirements, today! <br />
<br />
The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
<br />
These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables of approximately 50-80%. <br />
<br />
Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work in “steroids” using Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
<br />
These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
<br />
=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
<br />
== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
<br />
<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
'''<br />
</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
<br />
<br />
'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
<br />
'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
<br />
== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
<br />
<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
<br />
=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
<br />
CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
<br />
<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
<br />
'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
</li><br />
<br />
== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
<br />
This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
<br />
=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
</li><br />
<br />
<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikoshttps://crtc.cs.odu.edu/index.php?title=Main_Page&diff=9039Main Page2023-05-12T14:15:56Z<p>Nikos: /* COVID-19 */</p>
<hr />
<div><br />
<br />
[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]<br />
<br />
== Announcements == <br />
<br />
=== Congratulations to Dr. Thomadakis===<br />
[[File:Polykarpos-Grad.jpeg|200px|left]] <br />
I want to offer congratulations to Dr. Thomadakis for his impressive Ph.D. work and for defending and officially completing all his Ph.D. requirements, today! <br />
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The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million. <br />
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These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables of approximately 50-80%. <br />
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Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work in “steroids” using Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.<br />
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These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.<br />
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=== COVID-19 ===<br />
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].<br />
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== Introduction ==<br />
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-specific knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for specific supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].<br />
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<center><br />
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big><br />
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</center><br />
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]<br />
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'''Broader Impact'''<br />
The proposed activity will have a significant and broader impact on research and education, well beyond the bounds of this specific project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will benefit the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US. <br />
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'''Intellectual Merit'''<br />
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.<br />
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== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==<br />
<gallery heights=300px widths=300px mode="slideshow"><br />
File:telescopic_and_meshes.jpg<br />
File:CDT3D slide rocket nacelle.jpg <br />
File:Dlr airbus.jpg<br />
File:Scitech 2019 delta wing.jpg<br />
</gallery><br />
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<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level"><br />
File:CDT3D.jpg|130px|CDT3D Mesh<br />
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus <br />
File:Output_grid.jpg|130px|Rocket with Engine Mesh<br />
|- style="text-align: center;"<br />
||||'''CDT3D Meshes using speculative execution model at the chip level'''<br />
</gallery><br />
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=== Overview ===<br />
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations. <br />
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CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. <u> This is a joined project with NASA/LaRC and MIT. </u><br />
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<li style="display: inline-block;"><br />
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]<br />
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.<br />
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'''Objectives'''<br />
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.<br />
:# Development of error-based metrics to drive an anisotropic adaptive process<br />
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.<br />
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== Real-time Adaptive Mesh Generation in Medical Image Computing ==<br />
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This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI. <br />
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=== Overview === <br />
<li style="display: inline-block;"><br />
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]<br />
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. <u> This project is a joined with VCU, VA. </u><br />
</li><br />
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<li style="display: inline-block;"><br />
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]<br />
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. <u> This is a joined project with Harvard Medical School, MA. </u><br />
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<li style="display: inline-block;"><br />
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]<br />
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. <u> This is a joined project with Medical School at Stony Brook University, NY. </u><br />
</li></div>Nikos