Scalable GPU graph traversal

  • Authors:
  • Duane Merrill;Michael Garland;Andrew Grimshaw

  • Affiliations:
  • Unversity of Virginia, Charlottesville, VA, USA;NVIDIA Corporation, Santa Clara, CA, USA;University of Virginia, Charlottesville, VA, USA

  • Venue:
  • Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
  • Year:
  • 2012

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Abstract

Breadth-first search (BFS) is a core primitive for graph traversal and a basis for many higher-level graph analysis algorithms. It is also representative of a class of parallel computations whose memory accesses and work distribution are both irregular and data-dependent. Recent work has demonstrated the plausibility of GPU sparse graph traversal, but has tended to focus on asymptotically inefficient algorithms that perform poorly on graphs with non-trivial diameter. We present a BFS parallelization focused on fine-grained task management constructed from efficient prefix sum that achieves an asymptotically optimal O(|V|+|E|) work complexity. Our implementation delivers excellent performance on diverse graphs, achieving traversal rates in excess of 3.3 billion and 8.3 billion traversed edges per second using single and quad-GPU configurations, respectively. This level of performance is several times faster than state-of-the-art implementations both CPU and GPU platforms.