Efficient Breadth First Search on Multi-GPU Systems Using GPU-Centric OpenSHMEM

被引：2

作者：

Potluri, Sreeram ^{[1
]}

Goswami, Anshuman ^{[1
]}

Venkata, Manjunath Gorentla ^{[2
]}

Imam, Neena ^{[2
]}

机构：

[1] NVIDIA Corp, Santa Clara, CA 95051 USA

[2] Oak Ridge Natl Lab, Comp Sci & Math Div, Oak Ridge, TN USA

来源：

OPENSHMEM AND RELATED TECHNOLOGIES: BIG COMPUTE AND BIG DATA CONVERGENCE, OPENSHMEM 2017 | 2018年 / 10679卷

关键词：

D O I：

10.1007/978-3-319-73814-7_6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

NVSHMEM is an implementation of OpenSHMEM for NVIDIA GPUs which allows communication to be issued from inside CUDA kernels. In this work, we present an implementation of Breadth First Search for multi-GPU systems using NVSHMEM. We analyze the benefits and bottlenecks of moving fine-grained communication into CUDA kernels. Using our implementation of BFS, we achieve up to 75% improvement in performance compared to a CUDA-aware MPI-based implementation, in the best case.

引用

页码：82 / 96

页数：15

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