High performance graph analytics with productivity on hybrid CPU-GPU platforms

Abstract

In recent years, the rapid-growing scales of graphs have sparked a lot of parallel graph analysis frameworks to leverage the massive hardware resources on CPUs or GPUs. Existing CPU implementations are time-consuming, while GPU implementations are restricted by the memory space and the complexity of programming. In this paper, we present a high performance hybrid CPU-GPU parallel graph analytics framework with good productivity based on GraphMat. We map vertex programs to generalized sparse matrix vector multiplication on GPUs to deliver high performance, and propose a high-level abstraction for developers to implement various graph algorithms with relatively little efforts. Meanwhile, several optimizations have been adopted for reducing the communication cost and leveraging hardware resources, especially the memory hierarchy. We evaluate the proposed framework on three graph primitives (PageRank, BFS and SSSP) with large-scale graphs. The experimental results show that, our implementation achieves an average speedup of 7.0X than GraphMat on two 6-core Intel Xeon CPUs. It also has the capability to process larger datasets but achieves comparable performance than MapGraph, a state-of-the-art GPU-based framework.