UC Davis

For large-scale graph analytics on the GPU, the irregularity of dataaccess/control flow and the complexity of programming GPUs have been twosignificant challenges for developing a programmable high-performance graphlibrary. "Gunrock," our high-level bulk-synchronous graph-processing systemtargeting the GPU, takes a new approach to abstracting GPU graph analytics:rather than designing an abstraction around computation, Gunrock insteadimplements a novel data-centric abstraction centered on operations ona vertex or edge frontier. Gunrock achieves a balance between performance andexpressiveness by coupling high-performance GPU computing primitives andoptimization strategies with a high-level programming model that allowsprogrammers to quickly develop new graph primitives with small code size andminimal GPU programming knowledge. We evaluate Gunrock on five graphprimitives (BFS, BC, SSSP, CC, and PageRank) and show that Gunrock has onaverage at least an order of magnitude speedup over Boost and PowerGraph,comparable performance to the fastest GPU hardwired primitives, and betterperformance than any other GPU high-level graph library.