Cores - Revision history

Nikos at 11:56, 19 August 2022

2022-08-19T11:56:32Z

← Older revision		Revision as of 06:56, 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.

Ctsolakis: /* Tightly-coupled approaches */

2018-08-14T15:44:05Z

Tightly-coupled approaches

← Older revision		Revision as of 10:44, 14 August 2018
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	=== Tightly-coupled approaches ===		=== Tightly-coupled approaches ===
	This groups is experimented with two 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]		# 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]

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

2018-03-31T13:55:59Z

Real-time Medical Image Computing

← Older revision		Revision as of 08:55, 31 March 2018
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	= Parallel Runtime Software Systems =		= Parallel Runtime Software Systems =
	= Real-time Medical Image Computing =		= Real-time Medical Image Computing =
	[[~~RealTimeMedicalImageComputing~~ \| Real Time Medical Image Computing]]		[[Image-to-Mesh Conversion \| Real Time Medical Image Computing]]

Ctsolakis: /* Loosely-coupled approach */

2018-03-29T06:16:24Z

Loosely-coupled approach

← Older revision		Revision as of 01:16, 29 March 2018
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	=== Loosely-coupled approach ===		=== Loosely-coupled approach ===
	Loosely coupled using CDT3D ~~[MiniRTS workshop slides~~]		[[PREMA.CDT3D \| Loosely coupled using CDT3D ]]

	= Parallel Runtime Software Systems =		= Parallel Runtime Software Systems =
	= Real-time Medical Image Computing =		= Real-time Medical Image Computing =
	[[RealTimeMedicalImageComputing \| Real Time Medical Image Computing]]		[[RealTimeMedicalImageComputing \| Real Time Medical Image Computing]]

Ctsolakis: update links

2018-03-29T05:25:29Z

update links

← Older revision		Revision as of 00:25, 29 March 2018
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	:''' Data Decomposition'''		:''' Data Decomposition'''

	:# [[~~Cores/~~PDR \| Parallel Data Refinement using TetGen ]]		:# [[PDR.TetGen \| Parallel Data Refinement using TetGen ]]
	:# [[PDR.AFLR \| Parallel Data Refinement using AFLR]]		:# [[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]		:# 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]

Ctsolakis at 05:24, 29 March 2018

2018-03-29T05:24:19Z

← Older revision		Revision as of 00:24, 29 March 2018
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	== Parallel Adaptive Mesh Generation ==		= Parallel Adaptive Mesh Generation =

	==Introduction==		==Introduction==
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	Loosely coupled using CDT3D [MiniRTS workshop slides]		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]]		[[RealTimeMedicalImageComputing \| Real Time Medical Image Computing]]

Ctsolakis: /* Introduction */

2018-03-29T05:23:50Z

Introduction

← Older revision		Revision as of 00:23, 29 March 2018
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	==Introduction==		==Introduction==
			<li style="display: inline-block;">
			[[File:Telescopic.png\|frameless\|right\|600px]]

	Our long term goal is to achieve extreme-scale adaptive CFD simulations on the		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
	concurrency that exists at
	multiple levels in
	anisotropic and adaptive
	simulations. At the chip
	and node levels, the
	telescopic approach
	deploys a Parallel
	Optimistic (PO) layer and
	Parallel Data Refinement
	(PDR) layer, respectively
	~~(see Sections below)~~. In
	~~this paper~~, ~~we focus on~~ the ~~implementation of PDR. In future efforts, the PDR layer will~~
	~~be implemented on top of the PO layer [3]. The PDR layer relies on theory presented for~~
	~~parallel mesh generation and adaptivity [4] [5] [6].~~ The requirements for parallel mesh
	generation and adaptivity are:		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.
	1. 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:
	2. 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
	3. 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
	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.
	4. 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 experience from the PDR implementation suggests~~ that the code~~-reuse criterion~~
	~~ought to be adjusted for several reasons~~: ~~(i) complexity~~ of ~~industrial strength~~ sequential
	mesh generation ~~codes~~ and ~~(ii) their reproducibility properties~~.

	=== Tightly-coupled approaches ===		=== Tightly-coupled approaches ===
	This groups is experimented with two tightly-coupled approaches :		This groups is experimented with two tightly-coupled approaches :
	# CDT3D		# 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 ===		=== Partially-coupled approaches ===
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	:# [[Cores/PDR \| Parallel Data Refinement using TetGen ]]		:# [[Cores/PDR \| Parallel Data Refinement using TetGen ]]
	:# [[PDR.AFLR \| Parallel Data Refinement using AFLR]]		:# [[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'''		:''' 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.		:extension to 3D is in progress.

Ctsolakis: /* Partially-coupled approaches */

2018-03-28T15:41:56Z

Partially-coupled approaches

← Older revision		Revision as of 10:41, 28 March 2018
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	=== Partially-coupled approaches ===		=== Partially-coupled approaches ===

	: ~~====~~ Data Decomposition ~~====~~		:''' Data Decomposition'''

	:# [[Cores/PDR \| Parallel Data Refinement using TetGen ]]		:# [[Cores/PDR \| Parallel Data Refinement using TetGen ]]
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	:# 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 [LINK to Daming papers]

	: ~~====~~ Domain Decomposition ~~====~~		:''' Domain Decomposition'''
	:LINK ~~to 2d PCDM paper~~		: PCDM LINK TO PAPER
	:extension to 3D is in progress.		:extension to 3D is in progress.

Ctsolakis: /* Introduction */

2018-03-28T09:36:05Z

Introduction

@@ Line 5: / Line 5: @@
 ==Introduction==
-[add telescopic approach]
+Our long term goal is to achieve extreme-scale adaptive CFD simulations on the
 === Tightly-coupled approaches ===

Ctsolakis: /* Parallel Adaptive Mesh Generation */

2018-03-28T09:34:01Z

Parallel Adaptive Mesh Generation

← Older revision		Revision as of 04:34, 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 ===