Parallel breadth first search on GPU clusters

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Publication Type pre-print
School or College College of Engineering
Department Computing, School of
Creator Berzins, Martin
Other Author Fu, Zhisong; Dasari, Harish Kumar; Bebee, Bradley; Thompson, Bryan
Title Parallel breadth first search on GPU clusters
Date 2014-01-01
Description Fast, scalable, low-cost, and low-power execution of parallel graph algorithms is important for a wide variety of commercial and public sector applications. Breadth First Search (BFS) imposes an extreme burden on memory bandwidth and network communications and has been proposed as a benchmark that may be used to evaluate current and future parallel computers. Hardware trends and manufacturing limits strongly imply that many-core devices, such as NVIDIA® GPUs and the Intel ® Xeon Phi ® , will become central components of such future systems. GPUs are well known to deliver the highest FLOPS/watt and enjoy a very significant memory bandwidth advantage over CPU architectures. Recent work has demonstrated that GPUs can deliver high performance for parallel graph algorithms and, further, that it is possible to encapsulate that capability in a manner that hides the low level details of the GPU architecture and the CUDA language but preserves the high throughput of the GPU. We extend previous research on GPUs and on scalable graph processing on supercomputers and demonstrate that a high-performance parallel graph machine can be created using commodity GPUs and networking hardware.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 110
Last Page 118
Language eng
Bibliographic Citation Fu, Z., Dasari, H. K., Bebee, B., Berzins, M., & Thompson, B. (2014). Parallel breadth first search on GPU clusters. Proceedings - 2014 IEEE International Conference on Big Data, 7004219, 110-8.
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Format Medium application/pdf
Format Extent 1,006,175 bytes
Identifier uspace,19283
ARK ark:/87278/s6991h4s
Setname ir_uspace
Date Created 2015-02-17
Date Modified 2021-05-06
ID 712825
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