Par4all: Auto-Parallelizing C and Fortran for the CUDA Architecture

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•Introduction ◮ Through its Wild Systems subsidiary company • WildNode hardware desktop accelerator ◮ Low noise for in-office operation ◮ x86 manycore ◮ nVidia Tesla GPU Computing ◮ Linux & Windows • WildHive ◮ Aggregate 2-4 nodes with 2 possible memory views � Distributed memory with Ethernet or InfiniBand � Virtual shared memory through Linux Kerrighed for single-image system http://www.wild-systems.com �Par4All in CUDA — GPU conference 10/1/2009 HPC Project, Mines ParisTech, TÉLÉCOM Bretagne, RPI Ronan KERYELL et al. 2 / 46
•Introduction ◮ • Parallelize and optimize customer applications, co-branded as a bundle product in a WildNode (e.g. Presagis Stage battle-field simulator) • Acceleration software for the WildNode ◮ GPU-accelerated libraries for Matlab/Octave/R ◮ Transparent execution of Matlab on the WildNode • Remote display software for Windows on the WildNode HPC consulting • ◮ Optimization and parallelization of applications ◮ High Performance?... not only TOP500-class systems: power-efficiency, embedded systems, green computing... ◮ � Embedded system and application design ◮ Training in parallel programming (OpenMP, MPI, TBB, CUDA, OpenCL...) �Par4All in CUDA — GPU conference 10/1/2009 HPC Project, Mines ParisTech, TÉLÉCOM Bretagne, RPI Ronan KERYELL et al. 3 / 46
Page 1: • ◮ Par4All: Auto-Parallelizing
Page 5 and 6: •Par4All ◮ 1 Par4All 2 CUDA gen
Page 7 and 8: •Par4All ◮ (I) • PIPS (Interp
Page 9 and 10: •Par4All ◮ • Automatic parall
Page 11 and 12: •CUDA generation ◮ 1 Par4All 2
Page 13 and 14: •CUDA generation ◮ • Find par
Page 15 and 16: •CUDA generation ◮ (I) Parallel
Page 17 and 18: •CUDA generation ◮ (I) • Memo
Page 19 and 20: •CUDA generation ◮ (I) Conserva
Page 21 and 22: •CUDA generation ◮ • Hardware
Page 23 and 24: •CUDA generation ◮ (II) • So
Page 25 and 26: •CUDA generation ◮ • Reductio
Page 27 and 28: •CUDA generation ◮ (II) • For
Page 29 and 30: •CUDA generation ◮ (I) • CUDA
Page 31 and 32: •CUDA generation ◮ (III) 21 [..
Page 33 and 34: •Results ◮ 1 Par4All 2 CUDA gen
Page 35 and 36: •Results ◮ (I) Average time in
Page 37 and 38: •Results ◮ (I) Average time in
Page 39 and 40: •Conclusion ◮ 1 Par4All 2 CUDA
Page 41 and 42: •Conclusion ◮ Why open source?
Page 43 and 44: •Conclusion ◮ (II) • We have
Page 45 and 46: •Conclusion ◮ (II) • Capitali
Page 47 and 48: •Conclusion ◮ • HPC Project

Par4all: Auto-Parallelizing C and Fortran for the CUDA Architecture

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