Download PDF - Nvidia
Download PDF - Nvidia Download PDF - Nvidia
GPU Programming Approach • Design the computational algorithm just as you would with any parallel machine – Understand memory hierarchy • Size at each level • access characteristics • Bandwidth between levels – Stage data to computational engines efficiently 16
GPU Programming Heuristics • Thread & block indices are analogs of loop indices – Use the CPU as a controller and data stager • Keep the CPU out of the loops as much as possible • Keep main data structures in GPU memory • Use as few GPUs per task as possible • Rarely efficient to loop over streaming data from CPU – Work for a thread is generally a single inner loop 17
- Page 1 and 2: Industrial Seismic Imaging on a GPU
- Page 3 and 4: Seismic Imaging Data gas oil H 2 O
- Page 5 and 6: Seismic Imaging Data 5
- Page 7 and 8: Seismic Imaging Computational kerne
- Page 9 and 10: Seismic Imaging Theory 9
- Page 11 and 12: Seismic Imaging Why GPUs? • Price
- Page 13 and 14: Seismic Imaging Parallel algorithms
- Page 15: Seismic Imaging Hierarchical batch
- Page 19 and 20: GPU Programming Production codes
- Page 21 and 22: GPU Speed-up Kirchhoff Imaging Kern
- Page 23 and 24: Speedup “Reverse-time” Imaging
- Page 25 and 26: “Reverse-time” Imaging Inter-GP
- Page 27 and 28: Multi-GPU Performance Using MPI+Ope
- Page 29 and 30: Hess GPU Cluster • Got 32-node sy
- Page 31 and 32: Hess GPU Cluster • 2 nd generatio
- Page 33: Acknowledgements • Code co-author
GPU Programming<br />
Heuristics<br />
• Thread & block indices are analogs of loop<br />
indices<br />
– Use the CPU as a controller and data stager<br />
• Keep the CPU out of the loops as much as possible<br />
• Keep main data structures in GPU memory<br />
• Use as few GPUs per task as possible<br />
• Rarely efficient to loop over streaming data from CPU<br />
– Work for a thread is generally a single inner loop<br />
17
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