72. Multi-GPU Graph Analytics
Authors: 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)
Abstract: We present a single-node, multi-GPU programmable graph processing library that allows programmers to easily extend single-GPU graph algorithms to achieve scalable performance on large graphs with billions of edges. Our design only requires users to specify a few algorithm-dependent concerns, hiding most multi-GPU related implementation details. We analyze the theoretical and practical limits to scalability in the context of varying graph primitives and datasets. We describe several optimizations, including kernel fusion, direction optimized traversal, idempotence, and a just-enough memory allocation scheme, for better performance and smaller memory consumption. Compared to previous work, we achieve best-of-class performance across operations and datasets, including excellent strong and weak scalability on most primitives as we increase the number of GPUs in the system.
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