文章基本信息

标题：HPGraph: High-Performance Graph Analytics with Productivity on the GPU
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作者：Haoduo Yang ; Huayou Su ; Qiang Lan 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2018
卷号：2018
DOI：10.1155/2018/9340697
出版社：Hindawi Publishing Corporation
摘要：The growing use of graph in many fields has sparked a broad interest in developing high-level graph analytics programs. Existing GPU implementations have limited performance with compromising on productivity. HPGraph, our high-performance bulk-synchronous graph analytics framework based on the GPU, provides an abstraction focused on mapping vertex programs to generalized sparse matrix operations on GPU as the backend. HPGraph strikes a balance between performance and productivity by coupling high-performance GPU computing primitives and optimization strategies with a high-level programming model for users to implement various graph algorithms with relatively little effort. We evaluate the performance of HPGraph for four graph primitives (BFS, SSSP, PageRank, and TC). Our experiments show that HPGraph matches or even exceeds the performance of high-performance GPU graph libraries such as MapGraph, nvGraph, and Gunrock. HPGraph also runs significantly faster than advanced CPU graph libraries.