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It is common for real-world applications to analyze big graphs usingdistributed graph processing systems. Popular in-memory systems require anenormous amount of resources to handle big graphs. While several out-of-coreapproaches have been proposed for processing big graphs on disk, the high diskI/O overhead could significantly reduce performance. In this paper, we proposeGraphH to enable high-performance big graph analytics in small clusters.Specifically, we design a two-stage graph partition scheme to evenly divide theinput graph into partitions, and propose a GAB (Gather-Apply-Broadcast)computation model to make each worker process a partition in memory at a time.We use an edge cache mechanism to reduce the disk I/O overhead, and design ahybrid strategy to improve the communication performance. GraphH canefficiently process big graphs in small clusters or even a single commodityserver. Extensive evaluations have shown that GraphH could be up to 7.8x fastercompared to popular in-memory systems, such as Pregel+ and PowerGraph whenprocessing generic graphs, and more than 100x faster than recently proposedout-of-core systems, such as GraphD and Chaos when processing big graphs.

GraphH: High Performance Big Graph Analytics in Small Clusters