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Computation of Mutual Information Metric for Image Registration on Multiple GPUs

Computation of Mutual Information Metric for Image Registration on Multiple GPUs. Andrew V. Adinetz 1 , Markus Axer 2 , Marcel Huysegoms 2 , Stefan Köhnen 2 , Jiri Kraus 3 , Dirk Pleiter 1. 26.08.2013. 1 JSC, Forschungszentrum Jülich 2 INM-1, Forschungszentrum Jülich 3 NVIDIA GmbH.

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Computation of Mutual Information Metric for Image Registration on Multiple GPUs

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  1. Computation of Mutual Information Metricfor Image Registration on Multiple GPUs Andrew V. Adinetz1, Markus Axer2, Marcel Huysegoms2, Stefan Köhnen2, Jiri Kraus3, Dirk Pleiter1 26.08.2013 1 JSC, Forschungszentrum Jülich 2 INM-1, Forschungszentrum Jülich 3 NVIDIA GmbH

  2. Outline Brain Image Registration Multi-GPUImplementation systemmemory listupdate Performance Evaluation Conclusion

  3. Preparationofthebrain

  4. Pushingthelimitsfor a cellularbrainmodel • BigBrain – first high-resolution brain model at microscopical scale • 7404 histological sections stained for cell bodies • scanned with a flad bed scanner • original resolution 10 × 10 × 20 μm3(11.000 × 13.000 pixels) • downscaling to 20 μmisotropic • removal of artifacts • 1 Terabyte • in cooperation with Alan Evans, McGill, Montreal • Amunts et al. (2013)Science

  5. Image Registration The processofaligningimagesiscalledregistration ITK Workflow

  6. Mutual Information Metric i, j – pixelvalues (0 .. 255) successfulfor multi-modal registration

  7. Two Image Cross-Histogram for(inty = 0; y < fixed_sz_y; y++) for(intx = 0; x < fixed_sz_x; x++) { inti = bin(fixed[x, y]); float x1 = transform_x(x, y); float y1 = transform_y(x, y); intj = bin(interpolate(moving, x1, y1)); histogram[i, j]++; // atomic on GPU } maincomputationalkernel transformcanbecomplex (1000+ parameters) GPU implementation: 1 pixel/thread, atomics

  8. Large Data Size Large-area Polarimeter PolarizingMicroscope size: 3.000 × 3.000 px pixel size: 60 × 60 μm file size: 30 MB size: 100.000 × 100.000 px pixel size: 1.6 x 1.6 μm file size: 40 GB

  9. Multi-GPU Mutual Information Domain decomposition distributefixed and movingimages histogramcontributionssummed up Moving image: how to handle? irregularaccesspattern Approaches System memoryreplication (sysmem) Listupdate (listupdate)

  10. System MemoryReplication Replicateentiremoving image in pinnedhost RAM accessible to GPU + easy to implement – systemmemoryaccessesareslower – cannotusetextureinterpolation Optimizations moving image halo in GPU RAM

  11. Listupdate typedefstruct{ float[2] movingCoords; short destRank; char fixedBin; } message_t; Processing bufferremoteaccesses exchangebuffers computecontributionsremotely + computation-communicationoverlap – hard to implement – chunkprocessing (orwon‘t fit intobuffer) Optimizations buffers: AoS vs. SoA, atomics vs. grouping using multiple streams

  12. y ChunkProcessing and Overlap Fixed Image Fixed Image (0,0) x Group Group Exchange Handle messages Process chunk Process chunk 1 Group Exchange Process chunk 2

  13. Buffer Writeout: Atomics vs. Group atomics eachwritingthreadincrementsatomiccounter + simpler – atomicscanbe a bottleneck – onebuffer per receiverrequired grouping eachthreadwrites to fixedlocation buffersgroupedbeforesending + singlebuffer, lessmemory + optimizedgrouping (shared-memoryatomics, prefixsum) – morecomplicated (separate kernelrequired)

  14. Benchmarksetup Remote access Moving Image y Fixed Image Fixed Image Mask (0,0) x

  15. Test Hardware JUDGE 256-node GPU cluster Each M2070 node: • 2x M2070 (Fermi) GPU, each 6 GB RAM • 12-core X5650 CPU @ 2.67 GHz, 96 GB RAM JuHydra single-node Kepler machine • 2x K20X (Kepler) GPU, each 6 GB RAM • 16-core E5-2650 CPU @ 2 GHz, 64 GB RAM

  16. Baseline: Full Replication (M2070) ideal scalability

  17. Sysmem on Fermi

  18. Sysmem on Fermi: Explanation No sysmem Access Good Coalescing Few sysmem Access Bad Coalescing Manysysmem Access Bad Coalescing Most sysmem Access Good Coalescing

  19. Sysmem on Fermi: PCI-E Queries

  20. Sysmem: Halo Sizes mostly quantitative, not qualitative difference

  21. Listupdate: Multiple Streams 4 streams look the best

  22. Listupdate: AoSvsSoA, Atomicsvs Group SoA + atomics looks best

  23. Sysmem vs. Listupdate: Fermi on Fermi, sysmem is better

  24. Sysmem vs. Listupdate: Kepler (Closeup) on Kepler, listupdate is better

  25. Conclusions Fermi performancelimitedbyatomics systemmemoryreplicationisbetter Kepler order of magnitudefasterthanFermi no longerdominatedbyatomics listupdate (atomic, SoA, 4 streams) isbetter Future work Compression Trials on real images

  26. ? Questions INM-1 at FZJ: http://www.fz-juelich.de/inm/inm-1/EN/Home/home_node.html NVidiaApplication Lab at FZJ: http://www.fz-juelich.de/ias/jsc/nvlab Andrew V. Adinetz: adinetz@gmail.com Jiri Kraus: jkraus@nvidia.com Dirk Pleiter: d.pleiter@fz-juelich.de

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