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

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How fast? We should now check how fast our implementation goes CPU: Optimized serial code on i7 2.80GHz GPU: Tesla C2050 (Fermi) 448 cores @ 1.15GHz (use 48KB shared-memory) Benchmark with: 1000 × 1000 cells 2D Riemann problem initial data CPU time: 902.53s <strong>Finite</strong> <strong>Volume</strong> Methods Laboratory for Scientific Computing 12 / 22
How fast? We should now check how fast our implementation goes CPU: Optimized serial code on i7 2.80GHz GPU: Tesla C2050 (Fermi) 448 cores @ 1.15GHz (use 48KB shared-memory) Benchmark with: 1000 × 1000 cells 2D Riemann problem initial data CPU time: 902.53s GPU time: 9.6s (94.0×) <strong>Finite</strong> <strong>Volume</strong> Methods Laboratory for Scientific Computing 12 / 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: 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 and 44: Performance on different sized grid
Page 45 and 46: Summary Achieved 147x speed-up for
Page 47: Future investigations Efficient ext

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