Implementing Push-Pull Efficiently in GraphBLAS
University of California, Davis Lawrence Berkeley National Laboratory
arXiv:1804.03327 [cs.DC], (10 Apr 2018)
@article{yang2018implementing,
title={Implementing Push-Pull Efficiently in GraphBLAS},
author={Yang, Carl and Buluc, Aydin and Owens, John D.},
year={2018},
month={apr},
archivePrefix={"arXiv"},
primaryClass={cs.DC}
}
We factor Beamer’s push-pull, also known as direction-optimized breadth-first-search (DOBFS) into 3 separable optimizations, and analyze them for generalizability, asymptotic speedup, and contribution to overall speedup. We demonstrate that masking is critical for high performance and can be generalized to all graph algorithms where the sparsity pattern of the output is known a priori. We show that these graph algorithm optimizations, which together constitute DOBFS, can be neatly and separably described using linear algebra and can be expressed in the GraphBLAS linear-algebra-based framework. We provide experimental evidence that with these optimizations, a DOBFS expressed in a linear-algebra-based graph framework attains competitive performance with state-of-the-art graph frameworks on the GPU and on a multi-threaded CPU, achieving 101 GTEPS on a Scale 22 RMAT graph.
April 15, 2018 by hgpu
