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A Scalable Distributed Parallel Breadth-First Search Algorithm on BlueGene/L

Published: 12 November 2005 Publication History

Abstract

Many emerging large-scale data science applications require searching large graphs distributed across multiple memories and processors. This paper presents a distributed breadth- first search (BFS) scheme that scales for random graphs with up to three billion vertices and 30 billion edges. Scalability was tested on IBM BlueGene/L with 32,768 nodes at the Lawrence Livermore National Laboratory. Scalability was obtained through a series of optimizations, in particular, those that ensure scalable use of memory. We use 2D (edge) partitioning of the graph instead of conventional 1D (vertex) partitioning to reduce communication overhead. For Poisson random graphs, we show that the expected size of the messages is scalable for both 2D and 1D partitionings. Finally, we have developed efficient collective communication functions for the 3D torus architecture of BlueGene/L that also take advantage of the structure in the problem. The performance and characteristics of the algorithm are measured and reported.

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cover image ACM Conferences
SC '05: Proceedings of the 2005 ACM/IEEE conference on Supercomputing
November 2005
829 pages
ISBN:1595930612

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IEEE Computer Society

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Published: 12 November 2005

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SC '05 Paper Acceptance Rate 62 of 260 submissions, 24%;
Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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  • (2024)GraphCube: Interconnection Hierarchy-aware Graph ProcessingProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3627535.3638498(160-174)Online publication date: 2-Mar-2024
  • (2022)Scaling graph traversal to 281 trillion edges with 40 million coresProceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming10.1145/3503221.3508403(234-245)Online publication date: 2-Apr-2022
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