Gunrock: A High-Performance Graph Processing Library on the GPU
For large-scale graph analytics on the GPU, the irregularity of data access/control flow and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. “Gunrock,” our high-level bulk-synchronous graph-processing system targeting the GPU, takes a new approach to abstracting GPU graph analytics: rather than designing an abstraction around computation, Gunrock instead implements a novel data-centric abstraction centered on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high-performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We evaluate Gunrock on five graph primitives (BFS, BC, SSSP, CC, and PageRank) and show that Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives, and better performance than any other GPU high-level graph library.
Mon 14 MarDisplayed time zone: Belfast change
16:20 - 18:00 | GPUs and SchedulingMain conference at Mallorca+Menorca Chair(s): Christophe Dubach University of Edinburgh | ||
16:20 25mTalk | Gunrock: A High-Performance Graph Processing Library on the GPU Main conference Yangzihao Wang , Andrew Davidson University of California, Davis, Yuechao Pan University of California, Davis, Yuduo Wu University of California, Davis, Andy Riffel University of California, Davis, John D. Owens University of California, Davis Link to publication DOI | ||
16:45 25mTalk | GPU Multisplit Main conference Saman Ashkiani University of California, Davis, Andrew Davidson University of California, Davis, Ulrich Meyer Goethe-Universitat Frankfurt am Main, John D. Owens University of California, Davis Link to publication DOI | ||
17:10 25mTalk | Keep Calm and React with Foresight: Strategies for Low-Latency and Energy-Efficient Elastic Data Stream Processing Main conference Link to publication DOI | ||
17:35 25mTalk | Work Stealing for Interactive Services to Meet Target Latency Main conference Jing Li Washington University in St. Louis, Kunal Agrawal Washington University in St. Louis, Sameh Elnikety Microsoft Research, Yuxiong He Microsoft Research, I-Ting Angelina Lee Washington University in St. Louis, Chenyang Lu Washington University in St. Louis, Kathryn S McKinley Microsoft Research Link to publication DOI |