GPU Sparse Graph Traversal

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Summary BFS conclusions Duane Merrill, University of Virginia Quadratic-work approaches are uncompetitive – Need linear-work algorithm Dynamic workload management: Overall conclusions – Prefix sum instead of atomic read-modify-write ops – Fine-grained expansion and contraction for high utilization GPUs can be very amenable to dynamic, cooperative problems BFS is actually well-suited to GPU strengths – Exposes lots of concurrency – Is memory-bound Merrill et al. High performance and Scalable GPU Graph Traversal. Technical Report CS2011-05, Department of Computer Science, University of Virginia, Aug. 2011 20
Questions? Merrill, D., Garland, M., and Grimshaw, A. High Performance and Scalable GPU Graph Traversal. Technical Report CS2011-05, Department of Computer Science, University of Virginia. Aug. 2011. 21
Page 1 and 2: GPU Sparse Graph Traversal Duane Me
Page 3 and 4: The punchline “Conventional wisdo
Page 5 and 6: Level-synchronous BFS parallelizati
Page 7 and 8: Linear-work approach 1. Expand neig
Page 9 and 10: Vertices (millions) Goal: Demonstra
Page 11 and 12: Linear-work challenges for parallel
Page 13 and 14: Prefix sum for allocation Input (e.
Page 15 and 16: (3) Poor locality within SIMD lanes
Page 17 and 18: Simultaneous discovery Normally not
Page 19: Single-socket performance compariso
Page 23 and 24: Experimental corpus Graph Spy Plot
Page 25 and 26: Coupling of expansion & contraction
Page 27 and 28: Comparison of expansion techniques
Page 29 and 30: Comparison of coupling approaches 1
Page 31 and 32: CTA local prefix sum Duane Merrill,

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