GraphSD: A State and Dependency aware Out-of-Core Graph Processing System

Abstract

In recent years, system researchers have proposed many out-of-core graph processing systems to efficiently handle graphs that exceed the memory capacity of a single machine. Through disk-friendly graph data organizations and well-designed execution engines, existing out-of-core graph processing systems can maintain sequential locality on disk access and greatly reduce disk I/Os during processing. However, they have not fully explored the characteristics of graph data and algorithm execution to further reduce disk I/Os, leaving significant room for performance improvement. In this paper, we present a novel out-of-core graph processing system called GraphSD, which optimizes the I/O traffic by simultaneously capturing the state and dependency of graph data during computation. At the heart of GraphSD is a state- and dependency-aware update strategy that includes two adaptive update models, selective cross-iteration update (SCIU) and full cross-iteration update (FCIU). These two update models are dynamically triggered at runtime to enable active-vertex aware processing and cross-iteration vertex value computation, which avoid loading inactive edges and reduce disk I/Os in the future iterations. Moreover, an efficient sub-block based buffering scheme is proposed to further minimize I/O overheads. Our evaluation results show that GraphSD outperforms two state-of-the-art out-of-core graph processing systems HUS-Graph and Lumos by up to 2.7 × and 3.9 × respectively.