Recent graph computation approaches have demonstrated that a single PC can perform efficiently on billion-scale graphs. While these approaches achieve scalability by optimizing I/O operations, they do not fully exploit the capabilities of modern hard drives and processors. To overcome their performance, in this work, we introduce the Bimodal Block Processing (BBP), an innovation that is able to boost the graph computation by minimizing the I/O cost even further. With this strategy, we achieved the following contributions: (1) M-Flash, the fastest graph computation framework to date; (2) a flexible and simple programming model to easily implement popular and essential graph algorithms, including the first single-machine billion-scale eigensolver; and (3) extensive experiments on real graphs with up to 6.6 billion edges, demonstrating M-Flash's consistent and significant speedup.
@article{arxiv.1506.01406,
title = {M-Flash: Fast Billion-scale Graph Computation Using a Bimodal Block Processing Model},
author = {Hugo Gualdron and Robson Cordeiro and Jose Rodrigues-Jr and Duen Chau and Minsuk Kahng and U Kang},
journal= {arXiv preprint arXiv:1506.01406},
year = {2016}
}
Comments
Hugo Gualdron, Robson Cordeiro, Jose Rodrigues-Jr, Duen Chau, Minsuk Kahng, U Kang (2016) M-Flash: Fast Billion-scale Graph Computation Using a Bimodal Block Processing Model, In: ECML-PKDD16, pages 623-640, LNCS, Springer