Multi-GPU Graph Analytics
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}
}
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.
April 23, 2015 by hgpu
