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B. Hendrickson and R. Leland

Sandia National Laboratories, Albuquerque, United States of America

doi:10.4203/ccp.20.4.3

B. Hendrickson, R. Leland, "A Comparison of Problem Partitioning Algorithms for the Intel Paragon", in B.H.V. Topping, M. Papadrakakis, (Editors), "Advances in Parallel and Vector Processing for Structural Mechanics", Civil-Comp Press, Edinburgh, UK, pp 133-137, 1994. doi:10.4203/ccp.20.4.3

Abstract

To solve finite element or finite difference problems on parallel computers the data structures must be decomposed and partitioned among the processors. Efficient execution requires that this decomposition balance the computational load while minimizing the interprocessor communication. We empirically compare a variety of algorithms for this problem using Chaco, a publically available partitioning tool written by the authors. Algorithms are evaluated in terms of their computational cost and the quality of the partition as judged by the execution time of the application on the Intel Paragon parallel super computer.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 20 ADVANCES IN PARALLEL AND VECTOR PROCESSING FOR STRUCTURAL MECHANICS Edited by: B.H.V. Topping and M. Papadrakakis Paper IV.3 A Comparison of Problem Partitioning Algorithms for the Intel Paragon B. Hendrickson and R. Leland Sandia National Laboratories, Albuquerque, United States of America doi:10.4203/ccp.20.4.3 purchase the full-text of this paper Full Bibliographic Reference for this paper B. Hendrickson, R. Leland, "A Comparison of Problem Partitioning Algorithms for the Intel Paragon", in B.H.V. Topping, M. Papadrakakis, (Editors), "Advances in Parallel and Vector Processing for Structural Mechanics", Civil-Comp Press, Edinburgh, UK, pp 133-137, 1994. doi:10.4203/ccp.20.4.3 Abstract To solve finite element or finite difference problems on parallel computers the data structures must be decomposed and partitioned among the processors. Efficient execution requires that this decomposition balance the computational load while minimizing the interprocessor communication. We empirically compare a variety of algorithms for this problem using Chaco, a publically available partitioning tool written by the authors. Algorithms are evaluated in terms of their computational cost and the quality of the partition as judged by the execution time of the application on the Intel Paragon parallel super computer. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £60 +P&P)
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