Implementing Finite Volume algorithms on GPUs - many-core.group ...

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Speed-up demonstration pipeDemo.mpg <strong>Finite</strong> <strong>Volume</strong> Methods Laboratory for Scientific Computing 19 / 22
Summary Achieved 147x speed-up for <strong>Finite</strong> <strong>Volume</strong> solver For optimal performance, had to use templates and tuning of block-size. Future GPU-capable compilers/languages and APIs may be able to tune for this Will need significant effort in optimization technology Alternatively, develop C++ expression-template-like structures to do this automatically. <strong>Finite</strong> <strong>Volume</strong> Methods Laboratory for Scientific Computing 20 / 22
Page 1 and 2: Implementing <stro
Page 3 and 4: Applications of Finite</str
Page 5 and 6: Applications of Finite</str
Page 7 and 8: Shock-bubble simulation shockBubble
Page 9 and 10: Euler’s equations Flux-conservati
Page 11 and 12: Data layout in memory We use Struct
Page 13 and 14: MUSCL-Hancock scheme Piecewise-cons
Page 15 and 16: MUSCL-Hancock scheme Piecewise-cons
Page 17 and 18: Loading global data into shared mem
Page 19 and 20: Loading global data into shared mem
Page 21 and 22: Within shared memory Thread i − 1
Page 31 and 32: Thread-block layout Each cell depen
Page 33 and 34: Thread-block layout Each cell depen
Page 35 and 36: How fast? We should now check how f
Page 37 and 38: How fast? We should now check how f
Page 39 and 40: Templating over coordinate directio
Page 41 and 42: What block-size should we use? (x-d
Page 43: Performance on different sized grid
Page 47: Future investigations Efficient ext

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