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SCHEDULE: NOV 15-20, 2015
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Multi-GPU Graph Analytics
SESSION: Regular & ACM Student Research Competition Poster Reception
EVENT TYPE: Posters, Receptions, ACM Student Research Competition
EVENT TAG(S): HPC Beginner Friendly, Regular Poster
TIME: 5:15PM - 7:00PM
SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold
AUTHOR(S):Yuechao Pan, Yangzihao Wang, Yuduo Wu, Carl Yang, John D. Owens
ROOM:Level 4 - Lobby
ABSTRACT:
We present Gunrock, a multi-GPU graph processing library, that enables easy graph algorithm implementation and extension onto multiple GPUs, for scalable performance on large graphs with billions of edges. Our high-level data-centric abstraction focuses on vertex or edge frontier operations. With this abstraction, Gunrock balances between performance and low programming complexity, by coupling high performance GPU computing primitives and optimization strategies. Our multi-GPU framework only requires programmers to specify a few algorithm-dependent blocks, hiding most multi-GPU related implementation details. The framework effectively overlaps computation and communication, and implements a just-enough memory allocation scheme that allows memory usage to scale with more GPUs. We achieve 22GTEPS peak performance for BFS, which is the best of all single-node GPU graph libraries, and demonstrate a 6X speed-up with 2X total memory consumption on 8 GPUs. We identify synchronization / data communication patterns, graph topologies, and partitioning algorithms as limiting factors to further scalability.
Chair/Author Details:
Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|
Yuechao Pan - University of California, Davis
Yangzihao Wang - University of California, Davis
Yuduo Wu - University of California, Davis
Carl Yang - University of California, Davis
John D. Owens - University of California, Davis
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