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Graph Coloring on the GPU

  • Author(s): Osama, Muhammad
  • Truong, Minh
  • Yang, Carl
  • Buluç, Aydın
  • Owens, John D
  • et al.
Abstract

We design and implement parallel graph coloring algorithms on the GPU using two different abstractions—one datacentric (Gunrock), the other linear-algebra-based (GraphBLAS). We analyze the impact of variations of a baseline independent-set algorithm on quality and runtime. We study how optimizations such as hashing, avoiding atomics, and a max-min heuristic affect performance. Our Gunrock graph coloring implementation has a peak 2x speed-up, a geomean speed-up of 1.3x and produces 1.6x more colors over previous hardwired state-of-theart implementations on real-world datasets. Our GraphBLAS implementation of Luby’s algorithm produces 1.9x fewer colors than the previous state-of-the-art parallel implementation at the cost of 3x extra runtime, and 1.014x fewer colors than a greedy, sequential algorithm with a geomean speed-up of 2.6x.

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