Computational Technology Resources - CSETS

C. Moulinec¹, D.R. Emerson¹, Y. Fournier² and P. Vezolle³

¹STFC Daresbury Laboratory, SCD, Warrington, United Kingdom
²EDF R&D, MFEE, Chatou, France
³IBM France, La Pompignane, Montpellier, France

doi:10.4203/csets.31.2

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C. Moulinec, D.R. Emerson, Y. Fournier, P. Vezolle, "Challenges to be Overcome for Engineering Software to Run Efficiently on Petascale Machines", in B.H.V. Topping and P. Iványi, (Editor), "Developments in Parallel, Distributed, Grid and Cloud Computing for Engineering", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 2, pp 23-40, 2013. doi:10.4203/csets.31.2

Keywords: petascale, mesh generation, partitioning, solver, IO, computational fluid dynamics, PRACE.

Abstract

This chapter presents some of the challenges and solutions for engineering software to run efficiently on petascale machines. Generating several billion cell meshes is almost impossible in serial, and the way forward to cirvumvent this problem, apart from using one of the rare parallel mesh generators is to combine mesh joining and mesh multiplication. Partitioning these large meshes should be run parallel either by geometric or graph-based partitioners. For matrix-based resolution, using a multigrid algorithm as a preconditioner and a deflated conjugate gradient as a solver should allow good performance. Postprocessing should be run parallel by the code itself to minimise IOs, but how to deal with the restarting procedure remains an open problem. Finally, the software should also take into account the architecture of the machines because nodes may have considerable memory capacity available, as well as many cores and sometimes GPUs.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Computational Science, Engineering & Technology Series ISSN 1759-3158 CSETS: 31 DEVELOPMENTS IN PARALLEL, DISTRIBUTED, GRID AND CLOUD COMPUTING FOR ENGINEERING Edited by: B.H.V. Topping and P. Iványi Chapter 2 Challenges to be Overcome for Engineering Software to Run Efficiently on Petascale Machines C. Moulinec¹, D.R. Emerson¹, Y. Fournier² and P. Vezolle³ ¹STFC Daresbury Laboratory, SCD, Warrington, United Kingdom ²EDF R&D, MFEE, Chatou, France ³IBM France, La Pompignane, Montpellier, France doi:10.4203/csets.31.2 purchase the full-text of this chapter Full Bibliographic Reference for this chapter C. Moulinec, D.R. Emerson, Y. Fournier, P. Vezolle, "Challenges to be Overcome for Engineering Software to Run Efficiently on Petascale Machines", in B.H.V. Topping and P. Iványi, (Editor), "Developments in Parallel, Distributed, Grid and Cloud Computing for Engineering", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 2, pp 23-40, 2013. doi:10.4203/csets.31.2 Keywords: petascale, mesh generation, partitioning, solver, IO, computational fluid dynamics, PRACE. Abstract This chapter presents some of the challenges and solutions for engineering software to run efficiently on petascale machines. Generating several billion cell meshes is almost impossible in serial, and the way forward to cirvumvent this problem, apart from using one of the rare parallel mesh generators is to combine mesh joining and mesh multiplication. Partitioning these large meshes should be run parallel either by geometric or graph-based partitioners. For matrix-based resolution, using a multigrid algorithm as a preconditioner and a deflated conjugate gradient as a solver should allow good performance. Postprocessing should be run parallel by the code itself to minimise IOs, but how to deal with the restarting procedure remains an open problem. Finally, the software should also take into account the architecture of the machines because nodes may have considerable memory capacity available, as well as many cores and sometimes GPUs. purchase the full-text of this chapter (price £20) go to the previous chapter go to the next chapter return to the table of contents return to the book description purchase this book (price £85 +P&P)
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