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Scalable parallel minimum spanning forest computation

Published:25 February 2012Publication History
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Abstract

The proliferation of data in graph form calls for the development of scalable graph algorithms that exploit parallel processing environments. One such problem is the computation of a graph's minimum spanning forest (MSF). Past research has proposed several parallel algorithms for this problem, yet none of them scales to large, high-density graphs. In this paper we propose a novel, scalable, parallel MSF algorithm for undirected weighted graphs. Our algorithm leverages Prim's algorithm in a parallel fashion, concurrently expanding several subsets of the computed MSF. Our effort focuses on minimizing the communication among different processors without constraining the local growth of a processor's computed subtree. In effect, we achieve a scalability that previous approaches lacked. We implement our algorithm in CUDA, running on a GPU and study its performance using real and synthetic, sparse as well as dense, structured and unstructured graph data. Our experimental study demonstrates that our algorithm outperforms the previous state-of-the-art GPU-based MSF algorithm, while being several orders of magnitude faster than sequential CPU-based algorithms.

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        • Published in

          cover image ACM SIGPLAN Notices
          ACM SIGPLAN Notices  Volume 47, Issue 8
          PPOPP '12
          August 2012
          334 pages
          ISSN:0362-1340
          EISSN:1558-1160
          DOI:10.1145/2370036
          Issue’s Table of Contents
          • cover image ACM Conferences
            PPoPP '12: Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
            February 2012
            352 pages
            ISBN:9781450311601
            DOI:10.1145/2145816

          Copyright © 2012 ACM

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          • Published: 25 February 2012

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