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Billions of Migration Contributions per Second. Kirchhoff Imaging Kernel optimization 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 Performance GPU CPU 0 – Initial Kernel 1 – Used Texture Memory 2 – Used Shared Memory 3 – Global Memory Coalescing 4 – Decreased Data Trace Shared Memory Use 5 – Optimized Use of Shared Memory 6 – Consolidated “if” Statements, Eliminated or Substituted Some Math Operations 7 – Removed an “if” and “for” 8 – Used Texture Memory for Data- Trace Fetch 1.00 0.00 0 1 2 3 4 5 6 7 8 9 Code Version 20
GPU Speed-up Kirchhoff Imaging Kernel performance GPU-to-CPU Performance Ratio 100 Typical parameter range GT200 80 60 40 20 CUDA 2D Tex (nix=4) CUDA Linear Tex (nix=4) CUDA 2D Tex (niy=4) CUDA Linear Tex (niy=4) CUDA 2D Tex (nix=4) G2 CUDA 2D Tex (nix=4) G2 PIN CUDA Linear Tex (nix=4) G2 CUDA Linear Tex (nix=4) G2 PIN CUDA 2D Tex (niy=4) G2 CUDA 2D Tex (niy=4) G2 PIN CUDA Linear Tex (niy=4) G2 CUDA Linear Tex (niy=4) G2 PIN G80 0 2 4 6 8 10 12 14 16 18 image points per travel-time cell in x or y 21
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 and 16: Seismic Imaging Hierarchical batch
Page 17 and 18: GPU Programming Heuristics • Thre
Page 19: GPU Programming Production codes
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

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