LOSC

Abstract

Big data applications increasingly rely on the analysis of large graphs. In recent years, a number of out-of-core graph processing systems have been proposed to process graphs with billions of edges on just one commodity computer, by efficiently using the secondary storage (e.g., hard disk, SSD). On the other hand, the vertex-centric computing model is extensively used in graph processing thanks to its good applicability and expressiveness. Unfortunately, when implementing vertex-centric model for out-of-core graph processing, the large number of random memory accesses required to construct subgraphs lead to a serious performance bottleneck that substantially weakens cache access locality and thus leads to very long waiting time experienced by users for the computing results. In this paper, we propose an efficient out-of-core graph processing system, LOSC, to substantially reduce the overhead of subgraph construction without sacrificing the underlying vertex-centric computing model. LOSC proposes a locality-optimized subgraph construction scheme that significantly improves the in-memory data access locality of the subgraph construction phase. Furthermore, LOSC adopts a compact edge storage format and a lightweight replication of vertices to reduce I/O traffic and improve computation efficiency. Extensive evaluation results show that LOSC is respectively 6.9x and 3.5x faster than GraphChi and GridGraph, two state-of-the-art out-of-core systems.