13891

Multi-GPU Graph Analytics

Yuechao Pan, Yangzihao Wang, Yuduo Wu, Carl Yang, John D. Owens
University of California, Davis
arXiv:1504.04804 [cs.DC], (19 Apr 2015)

@article{pan2015multigpu,

   title={Multi-GPU Graph Analytics},

   author={Pan, Yuechao and Wang, Yangzihao and Wu, Yuduo and Yang, Carl and Owens, John D.},

   year={2015},

   month={apr},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

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We present a multi-GPU graph processing library that allows programmers to easily extend single-GPU graph algorithms to achieve scalable performance on large graph datasets with billions of edges. Our design only requires users to specify a few algorithm-dependent blocks, hiding most multi-GPU related implementation details. Our design effectively overlaps computation and data transfer and implements a just-enough memory allocation scheme that allows memory usage to scale with more GPUs. We achieve ~20 GTEPS peak performance for BFS, demonstrating a ~6X speed-up with ~2X total GPU memory consumption on 8 GPUs. We identify synchronization/data communication patterns, graph topologies, and partitioning algorithms as limiting factors to further scalability.
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