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

More documents

Recommendations

Info

•Par4All ◮ Hardware is moving quite (too) fast but... What has survived for 50+ years? Fortran programs... What has survived for 30+ years? C programs... • A lot of legacy code could be pushed onto parallel hardware (accelerators) with automatic tools... • Not as efficient as hand-tuned programs, but quick production phase • Need automatic tools for source-to-source transformation to leverage existing software tools for a given hardware �Par4All in CUDA — GPU conference 10/1/2009 HPC Project, Mines ParisTech, TÉLÉCOM Bretagne, RPI Ronan KERYELL et al. 6 / 46
•Par4All ◮ (I) • PIPS (Interprocedural Parallelizer of Scientific Programs): Open Source project from Mines ParisTech... ≈ 150 hy, 21-year old! • Funded by many people (French DoD, Industry & Research Departments, University, CEA, IFP, Onera, ANR (French NSF), European projects, regional research clusters...) • Project that coined polytope model-based compilation • ≈ 456 KLOC according to David A. Wheeler’s SLOCCount • ... but modular and sensible approach to pass through the years ◮ ≈300 phases (parsers, analyzers, transformations, optimizers, parallelizers, code generators, pretty-printers...) that can be combined for the right purpose ◮ NewGen object description language for language-agnostic automatic generation of methods, persistence, object introspection, visitors, accessors, constructors, XML marshaling for interfacing with external tools... �Par4All in CUDA — GPU conference 10/1/2009 HPC Project, Mines ParisTech, TÉLÉCOM Bretagne, RPI Ronan KERYELL et al. 7 / 46
Page 1 and 2: • ◮ Par4All: Auto-Parallelizing
Page 3 and 4: •Introduction ◮ • Parallelize
Page 5: •Par4All ◮ 1 Par4All 2 CUDA gen
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

Create successful ePaper yourself

Delete template?

Save as template?