GraphM

Abstract

With the rapidly growing demand of graph processing in the real world, a large number of iterative graph processing jobs run concurrently on the same underlying graph. However, the storage engines of existing graph processing frameworks are mainly designed for running an individual job. Our studies show that they are inefficient when running concurrent jobs due to the redundant data storage and access overhead. To cope with this issue, we develop an efficient storage system, called GraphM. It can be integrated into the existing graph processing systems to efficiently support concurrent iterative graph processing jobs for higher throughput by fully exploiting the similarities of the data accesses between these concurrent jobs. GraphM regularizes the traversing order of the graph partitions for concurrent graph processing jobs by streaming the partitions into the main memory and the Last-Level Cache (LLC) in a common order, and then processes the related jobs concurrently in a novel fine-grained synchronization. In this way, the concurrent jobs share the same graph structure data in the LLC/memory and also the data accesses to the graph, so as to amortize the storage consumption and the data access overhead. To demonstrate the efficiency of GraphM, we plug it into state-of-the-art graph processing systems, including GridGraph, GraphChi, PowerGraph, and Chaos. Experiments results show that GraphM improves the throughput by 1.73~13 times.