MMAP: Mining Billion-Scale Graphs on a PC with Fast, Minimalist Approach via Memory Mapping

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Sabrin, Kaeser Md.
Lin, Zhiyuan
Chau, Duen Horng
Lee, Ho
Kang, U.
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Large graphs with billions of nodes and edges are increasingly common, calling for new kinds of scalable computation frameworks. State-of-the-art approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can efficiently perform advanced computation on billion-node graphs. Although fast, they use sophisticated data structures, explicit memory management, and optimization techniques to achieve high speed and scalability. We propose a minimalist approach that forgoes such complexities, by leveraging the fundamental memory mapping (MMap) capability found on operating systems. We present multiple, major findings; we contribute: (1) our crucial insight that MMap can be a viable technique for creating fast, scalable graph algorithms that surpass some of the best techniques; (2) a counterintuitive result that we can do less and gain more ; MMap enables us to use a much simpler data structure (edge list) and algorithm design, and to defer memory management to the OS, while offering significantly faster or comparable performance as highly-optimized methods (e.g., 10 X as fast as GraphChi PageRank on 1.47 billion edge Twitter graph); (3) we performed extensive experiments on real and synthetic graphs, including the 6.6 billion edge YahooWeb graph, and show that MMap’s benefits sustain in most conditions. We hope this work will inspire others to explore how memory mapping may help improve other methods or algorithms to further increase their speed and scalability.
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