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V. Szeremi¹, L. Anton¹, C. Evangelinos², C. Moulinec¹ and Y. Fournier³

¹STFC Daresbury Laboratories, Warrington, United Kingdom
²IBM Research, Cambridge, Massachusetts, United States of America
³EDF R&D, Département Mécanique des Fluides, Energies et Environnement, Chatou Cedex, France

doi:10.4203/ccp.107.28

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V. Szeremi, L. Anton, C. Evangelinos, C. Moulinec, Y. Fournier, "Parallel Sparse Matrix Vector Product with OpenMP for SMPs in Code_Saturne", in , (Editors), "Proceedings of the Fourth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 28, 2015. doi:10.4203/ccp.107.28

Keywords: computational fluid dynamics, Code_Saturne, OpenMP, sparse matrix vector product, parallel algorithms, load balancing.

Summary

In this paper a new blocked sparse matrix vector product parallel algorithm based on Code_ Saturne native matrix format is proposed in order to improve the OpenMP scalability. New sparse matrix storage options based on the native matrix format, and corresponding algorithms, are implemented in Code_Saturne. In addition, trace-guided optimisations for reduced synchronisation and better load balance are proposed and their efficiency is investigated on different processor architectures. Results are presented for a range of systems, including architectures of PRACE Tier-0 machines, IBM Blue Gene/Q and iDataPlex (Sandybridge, Ivybridge) and Cray XC30 (Ivybridge). Initial results indicate that the new algorithm has a significantly better parallel performance across the tested hardware with respect to the native OpenMP sparse matrix vector product algorithm.

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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: 107 PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, GRID AND CLOUD COMPUTING FOR ENGINEERING Edited by: Paper 28 Parallel Sparse Matrix Vector Product with OpenMP for SMPs in Code_Saturne V. Szeremi¹, L. Anton¹, C. Evangelinos², C. Moulinec¹ and Y. Fournier³ ¹STFC Daresbury Laboratories, Warrington, United Kingdom ²IBM Research, Cambridge, Massachusetts, United States of America ³EDF R&D, Département Mécanique des Fluides, Energies et Environnement, Chatou Cedex, France doi:10.4203/ccp.107.28 purchase the full-text of this paper Full Bibliographic Reference for this paper V. Szeremi, L. Anton, C. Evangelinos, C. Moulinec, Y. Fournier, "Parallel Sparse Matrix Vector Product with OpenMP for SMPs in Code_Saturne", in , (Editors), "Proceedings of the Fourth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 28, 2015. doi:10.4203/ccp.107.28 Keywords: computational fluid dynamics, Code_Saturne, OpenMP, sparse matrix vector product, parallel algorithms, load balancing. Summary In this paper a new blocked sparse matrix vector product parallel algorithm based on Code_ Saturne native matrix format is proposed in order to improve the OpenMP scalability. New sparse matrix storage options based on the native matrix format, and corresponding algorithms, are implemented in Code_Saturne. In addition, trace-guided optimisations for reduced synchronisation and better load balance are proposed and their efficiency is investigated on different processor architectures. Results are presented for a range of systems, including architectures of PRACE Tier-0 machines, IBM Blue Gene/Q and iDataPlex (Sandybridge, Ivybridge) and Cray XC30 (Ivybridge). Initial results indicate that the new algorithm has a significantly better parallel performance across the tested hardware with respect to the native OpenMP sparse matrix vector product algorithm. 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 £45 +P&P)
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