Cores - Revision history

Nikos at 11:56, 19 August 2022

2022-08-19T11:56:32Z

Ctsolakis: /* Tightly-coupled approaches */

2018-08-14T15:44:05Z

‎Tightly-coupled approaches

Ctsolakis: /* Real-time Medical Image Computing */

2018-03-31T13:55:59Z

‎Real-time Medical Image Computing

Ctsolakis: /* Loosely-coupled approach */

2018-03-29T06:16:24Z

‎Loosely-coupled approach

Ctsolakis: update links

2018-03-29T05:25:29Z

update links

Ctsolakis at 05:24, 29 March 2018

2018-03-29T05:24:19Z

Ctsolakis: /* Introduction */

2018-03-29T05:23:50Z

‎Introduction

Ctsolakis: /* Partially-coupled approaches */

2018-03-28T15:41:56Z

‎Partially-coupled approaches

Ctsolakis: /* Introduction */

2018-03-28T09:36:05Z

‎Introduction

Ctsolakis: /* Parallel Adaptive Mesh Generation */

2018-03-28T09:34:01Z

‎Parallel Adaptive Mesh Generation

← Older revision		Revision as of 11:56, 19 August 2022
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−	~~Each~~ page lists the activities within the different cores for metrics-driven extreme scale parallel anisotropic mesh generation	+	This page lists some of the past activities within the different cores for metrics-driven extreme-scale parallel anisotropic mesh generation. The current focus is on quantum computing and the core thrusts will be posted soon.

@@ Line 35: / Line 35: @@
 === Tightly-coupled approaches ===
 This groups is experimented with two tightly-coupled approaches :
-# CDT3D
+# [[Isotropic Mesh Generation |  CDT3D]]
 # Parallel Optimistic Delaunay Mesh Generation for medical images, for details see [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=High%20Quality%20Real-Time%20Image-to-Mesh%20Conversion%20for%20Finite%20Element%20Simulations&paper_type=2&from=year]

@@ Line 56: / Line 56: @@
 = Parallel Runtime Software Systems =
 = Real-time Medical Image Computing =
-[[RealTimeMedicalImageComputing | Real Time Medical Image Computing]]
+[[Image-to-Mesh Conversion | Real Time Medical Image Computing]]

@@ Line 52: / Line 52: @@
 === Loosely-coupled approach ===
-Loosely coupled using CDT3D [MiniRTS workshop slides]
+[[PREMA.CDT3D | Loosely coupled using CDT3D ]]
 = Parallel Runtime Software Systems =
 = Real-time Medical Image Computing =
 [[RealTimeMedicalImageComputing | Real Time Medical Image Computing]]

@@ Line 42: / Line 42: @@
 :''' Data Decomposition'''
-:# [[Cores/PDR | Parallel Data Refinement using TetGen ]]
+:# [[PDR.TetGen | Parallel Data Refinement using TetGen ]]
 :# [[PDR.AFLR  | Parallel Data Refinement using AFLR]]
 :# Parallel Data Refinement using Parallel Optimistic Delaunay Mesh Generation for medical images , for details see [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%203D%20Hybrid%20Parallel%20Delaunay%20Image-to-Mesh%20Conversion%20Algorithm%20for%20Distributed%20Shared%20Memory%20Architectures&paper_type=2&from=year] and  [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=A%20Hybrid%20Parallel%20Delaunay%20Image-to-Mesh%20Conversion%20Algorithm%20Scalable%20on%20Distributed-Memory%20Clusters&paper_type=2&from=year]

@@ Line 2: / Line 2: @@
-== Parallel Adaptive Mesh Generation ==
+= Parallel Adaptive Mesh Generation =
 ==Introduction==
@@ Line 54: / Line 54: @@
 Loosely coupled using CDT3D [MiniRTS workshop slides]
-== Parallel Runtime Software Systems ==
+= Parallel Runtime Software Systems =
-== Real-time Medical Image Computing ==
+= Real-time Medical Image Computing =
 [[RealTimeMedicalImageComputing | Real Time Medical Image Computing]]

@@ Line 5: / Line 5: @@
 ==Introduction==
 Our long term goal is to achieve extreme-scale adaptive CFD simulations on the
-complex, heterogeneous
+complex, heterogeneous High Performance Computing (HPC) platforms. To achieve this
-High Performance
+goal, we proposed in a telescopic approach (see
-Computing (HPC)
+Figure) [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Telescopic%20Approach%20for%20Extreme-scale%20Parallel%20Mesh%20Generation%20for%20CFD%20Applications&paper_type=1&from=year]. The telescopic approach is critical in leveraging the
-platforms. To achieve this
+concurrency that exists at multiple levels in anisotropic and adaptive
-goal, we proposed in a
+simulations. At the chip and node levels, the telescopic approach
-telescopic approach (see
+deploys a Parallel Optimistic (PO) layer and Parallel Data Refinement
-Figure 1) [1] [2]. The
+(PDR) layer, respectively. Finally, between different nodes  the loosely-coupled approach is employed
 telescopic approach is
-critical in leveraging the
+The requirements for parallel mesh
 generation and adaptivity are:
-Figure 1 Telescopic Approach to parallel mesh generation and adaptation and
+# '''Stability:''' The quality of the mesh generated in parallel must be comparable to that of a mesh generated sequentially. The quality is defined in terms of the shape of the elements (using a chosen location/error-dependent metric), and the number of the elements (fewer is better for the same shape constraint).
-PDR layer in the middle.
+# '''Robustness:''' the ability of the software to correctly and efficiently process any input data. Operator intervention into a massively parallel computation is not only highly expensive, but most likely infeasible due to the large number of concurrently processed sub-problems.
-. Stability: the quality of the mesh generated in parallel must be comparable to that
+# '''Code re-use:''' a modular design of the parallel software that builds upon previously designed sequential meshing code, such that it can be replaced and/or updated with a minimal effort. Code re-use is feasible only if the code satisfies the reproducibility criterion, identified for the first time in this project. However, the experience from this project indicated that the complexity of state-of-the-art codes inhibits their modifications to a degree that their integration with parallel frameworks like PDR becomes impractical.
-of a mesh generated sequentially. The quality is defined in terms of the shape of
+# '''Scalability:''' the ratio of the time taken by the best sequential implementation to the time taken by the parallel implementation. The speedup is always limited by the inverse of the sequential  fraction of the software, and therefore all non-trivial stages of the computation must be parallelized to leverage the current architectures with millions of cores.
-the elements (using a chosen location/error-dependent metric), and the number of
+# '''Reproducibility:''' Which can be expressed into two forms:
-the elements (fewer is better for the same shape constraint).
+:#''Strong Reproducibility'' requires that the sequential mesh generation code, when executed with the same input, produces identical results under the following modes of execution:
-. Robustness: the ability of the software to correctly and efficiently process any
+:## continuous without restarts,and
-input data. Operator intervention into a massively parallel computation is not only
+:## with restarts and reconstructions of the internal data structures.
-highly expensive, but most likely infeasible due to the large number of
+:#''Weak Reproducibility'' requires that the sequential mesh generation code, when executed with the same input, produces results of the same quality under the following modes of execution:
-concurrently processed sub-problems.
+:## continuous without restarts, and
-. Code re-use: a modular design of the parallel software that builds upon
+:## with restarts and reconstructions of the internal data structures.
-previously designed sequential meshing code, such that it can be replaced and/or
+</li>
 updated with a minimal effort. Code re-use is feasible only if the code satisfies the
 === Tightly-coupled approaches ===
 This groups is experimented with two tightly-coupled approaches :
 # CDT3D
-# Parallel Optimistic Delaunay Mesh Generation for medical images, for details see [LINK]
+# Parallel Optimistic Delaunay Mesh Generation for medical images, for details see [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=High%20Quality%20Real-Time%20Image-to-Mesh%20Conversion%20for%20Finite%20Element%20Simulations&paper_type=2&from=year]
 === Partially-coupled approaches ===
@@ Line 67: / Line 44: @@
 :# [[Cores/PDR | Parallel Data Refinement using TetGen ]]
 :# [[PDR.AFLR  | Parallel Data Refinement using AFLR]]
-:# Parallel Data Refinement using Parallel Optimistic Delaunay Mesh Generation for medical images , for details see [LINK to Daming papers]
+:# Parallel Data Refinement using Parallel Optimistic Delaunay Mesh Generation for medical images , for details see [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%203D%20Hybrid%20Parallel%20Delaunay%20Image-to-Mesh%20Conversion%20Algorithm%20for%20Distributed%20Shared%20Memory%20Architectures&paper_type=2&from=year] and  [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=A%20Hybrid%20Parallel%20Delaunay%20Image-to-Mesh%20Conversion%20Algorithm%20Scalable%20on%20Distributed-Memory%20Clusters&paper_type=2&from=year]
 :''' Domain Decomposition'''
-: PCDM LINK TO PAPER
+: [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Algorithm%20872:%20Parallel%202D%20Constrained%20Delaunay%20Mesh%20Generation&paper_type=2&from=year PCDM]
 :extension to 3D is in progress.

← Older revision		Revision as of 09:34, 28 March 2018
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	== Parallel Adaptive Mesh Generation ==		== Parallel Adaptive Mesh Generation ==
		+
		+	==Introduction==
		+	[add telescopic approach]

	=== Tightly-coupled approaches ===		=== Tightly-coupled approaches ===