![]() Distributed frameworks have become popular choices prominent examples include GraphLab, PEGASUS, and Pregel. Such graphs’ sheer sizes call for new kinds of scalable computation frameworks. ![]() Large graphs with billions of nodes and edges are increasingly common in many domains, ranging from computer science, physics, chemistry, to bioinformatics. We believe our work provides a new direction in the design and development of scalable algorithms. We contribute: (1) a new insight that MMap is a viable technique for creating fast and scalable graph algorithms that surpasses some of the best techniques (2) the design and implementation of popular graph algorithms for billion-scale graphs with little code, thanks to memory mapping (3) extensive experiments on real graphs, including the 6.6 billion edge YahooWeb graph, and show that this new approach is significantly faster or comparable to the highly-optimized methods (e.g., 9.5× faster than GraphChi for computing PageRank on 1.47B edge Twitter graph). We propose a minimalist approach that forgoes such requirements, by leveraging the fundamental memory mapping (MMap) capability found on operating systems. ![]() To achieve high speed and scalability, they often need sophisticated data structures and memory management strategies. #Memory on disk graph PcGraph computation approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can perform efficient computation on billion-node graphs. ![]()
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