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Processing a one trillion-edge graph has recently been demonstrated by distributed graph engines running on clusters of tens to hundreds of nodes. In this paper, we employ a single heterogeneous machine with fast storage media (e.g., NVMe SSD) and massively parallel coprocessors (e.g., Xeon Phi) to reach similar dimensions. By fully exploiting the heterogeneous devices, we design a new graph processing engine, named MOSAIC, for a single machine. We propose a new locality-optimizing, space-efficient graph representation-Hilbert-ordered tiles, and a hybrid execution model that enables vertex-centric operations in fast host processors and edge-centric operations in massively parallel coprocessors.Our evaluation shows that for smaller graphs, MOSAIC consistently outperforms other state-of-the-art out-of-core engines by 3.2-58.6× and shows comparable performance to distributed graph engines. Furthermore, MOSAIC can complete one iteration of the Pagerank algorithm on a trillionedge graph in 21 minutes, outperforming a distributed diskbased engine by 9.2×.

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