Duane Merrill (NVIDIA) - GPU Technology Conference

More documents

Recommendations

Info

SummaryPurely data-parallel approaches can beuncompetitiveE.g., one thread per vertex/edge @ each timestepDynamic workload management:Prefix sum (vs. atomic-add)Utilization requires fine-grained expansion andcontraction<strong>GPU</strong>s can be very amenable to dynamic,cooperative problems46
dumerrill@nvidia.com
Page 1 and 2: Duane Merrill (NVIDIA)Michael Garla
Page 4 and 5: Sparse graphsGraph G(V, E) where n
Page 6: Compressed sparse row (CSR) represe
Page 9 and 10: Breadth-first search (BFS)1. Pick a
Page 11 and 12: Breadth-first search (BFS)1. Pick a
Page 13 and 14: BFS applicationsAlgorithmic primiti
Page 15 and 16: Common parallelization strategies
Page 17 and 18: Quadratic-work approach1. Inspect e
Page 19 and 20: Quadratic-work approach1. Inspect e
Page 21 and 22: Vertices (millions)Vertices (millio
Page 23 and 24: Referenced vertex identifiers(milli
Page 25 and 26: Linear-work challenges for parallel
Page 27 and 28: Prefix sum for allocationAllocation
Page 29 and 30: Data-flow of efficient CTA scan(gra
Page 31 and 32: 2) Poor load balancing within SIMD
Page 33 and 34: 4) SIMD simultaneous discovery0 1 2
Page 35 and 36: Single-socket comparisonGraphSpy Pl
Page 38 and 39: Maximal independent setA vertex sub
Page 40 and 41: MIS applicationsGraph coloring (ver
Page 42 and 43: The sequential algorithm• Repeate
Page 44 and 45: Data-parallel GPU approach• Input
Page 48 and 49: GTC 2013:The Premier Event in Accel
Page 50 and 51: Comparison of expansion techniques5
Page 52 and 53: 10 9 edges / secnormalizedCompariso
Page 54: 10 9 edges / secnormalizedMulti-GPU

Duane Merrill (NVIDIA) - GPU Technology Conference

Create successful ePaper yourself

Delete template?

Save as template?