Segmented Scan - Nvidia

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Simple Parallel Inclusive <strong>Scan</strong> Code __device__ int scan(int *p) { for (int offset=1; offset=offset) t = p[i-offset]; __syncthreads(); if(i>=offset) p[i]= t + p[i]; __syncthreads(); } }
Warp Speed • Warp is physical unit of CUDA parallelism — 32 threads that execute instructions synchronously • Synchronicity of warps can be leveraged for performance — When sharing data within a warp, don’t need __syncthreads() • ―Warp-Synchronous Programming‖ — (Powerful, but take care to avoid race conditions!) Sengupta, Harris, Garland, Owens. Efficient parallel scan algorithms for many-core GPUs. Scientic Computing with Multicore and Accelerators, chapter 19. 2011 (to appear)
Page 1 and 2: State of the Art in GPU Data-Parall
Page 3 and 4: Stream Parallelism Input Thread Thr
Page 5 and 6: Beyond streaming • GPUs are obvio
Page 7 and 8: Parallel Patterns • Many parallel
Page 9 and 10: Parallel Reduction: Easy 3 1 7 0 4
Page 11 and 12: Split Input Output • Example: rad
Page 13 and 14: Compact • Remove unneeded or inva
Page 15 and 16: “Where do I write my output?”
Page 17 and 18: Segmented Scan Segment Head Flags I
Page 19 and 20: Scan Applications
Page 21 and 22: Compact using Scan • Flag unneede
Page 23 and 24: Radix Sort / Split using Scan i = i
Page 25 and 26: CUDA Radix Sort • Sort blocks in
Page 27 and 28: Designing Sorting Algorithms for GP
Page 29 and 30: Building Trees • The split primit
Page 31 and 32: HLBVH • Improvement over LBVH —
Page 33 and 34: Breadth-First Search Order • BFS
Page 35 and 36: Parallel Hashing • Dense data str
Page 37 and 38: Cuckoo Hashing • Sequential inser
Page 39 and 40: GPU Parallel Hash Performance D. A.
Page 41 and 42: Implementing Scan: Strategies
Page 43 and 44: 1. Scan All Sub-blocks 3 3 3 3 3 5
Page 45 and 46: + + + + + + 1. Reduce All Sub-Block
Page 47 and 48: Implementing Scan In CUDA
Page 49: Sequential Inclusive Scan Code int
Page 53 and 54: Intra-block Scan using Intra-warp S
Page 55 and 56: Binary Warp Scan Code __device__ un
Page 57 and 58: Binary Reduction • Count the numb
Page 59 and 60: No need to re-implement • Open so
Page 61 and 62: CUDPP Example CUDPPConfiguration co
Page 63 and 64: Thrust Example // generate 16M rand
Page 65 and 66: “In general, the problem of defin
Page 67 and 68: Thank You! • Duane Merrill, David
Page 69: Scan Literature (2) • Sengupta et

Segmented Scan - Nvidia

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