sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
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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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