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A Simple Yet Effective Balanced Edge Partition Model for Parallel Computing

A Simple Yet Effective Balanced Edge Partition Model for Parallel Computing A Simple Yet E ective Balanced Edge Partition Model for Parallel Computing LINGDA LI , Brookhaven National Lab ROBEL GEDA, Rutgers University ARI B. HAYES, Rutgers University YANHAO CHEN, Rutgers University PRANAV CHAUDHARI, Rutgers University EDDY Z. ZHANG, Rutgers University MARIO SZEGEDY, Rutgers University Graph edge partition models have recently become an appealing alternative to graph vertex partition models for distributed computing due to their exibility in balancing loads and their performance in reducing communication cost [6, 16]. In this paper, we propose a simple yet e ective graph edge partitioning algorithm. In practice, our algorithm provides good partition quality (and better than similar state-of-the-art edge partition approaches, at least for power-law graphs) while maintaining low partition overhead. In theory, previous work [6] showed that an approximation guarantee of O(dmax log n log k ) apply to the graphs with m = (k 2 ) edges (k is the number of partitions). We further rigorously proved that this approximation guarantee hold for all graphs. We show how our edge partition model can be applied to parallel computing. We draw our example from GPU program locality enhancement and demonstrate that the graph edge partition model does not only apply http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of the ACM on Measurement and Analysis of Computing Systems Association for Computing Machinery

A Simple Yet Effective Balanced Edge Partition Model for Parallel Computing

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
2476-1249
DOI
10.1145/3084451
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Abstract

A Simple Yet E ective Balanced Edge Partition Model for Parallel Computing LINGDA LI , Brookhaven National Lab ROBEL GEDA, Rutgers University ARI B. HAYES, Rutgers University YANHAO CHEN, Rutgers University PRANAV CHAUDHARI, Rutgers University EDDY Z. ZHANG, Rutgers University MARIO SZEGEDY, Rutgers University Graph edge partition models have recently become an appealing alternative to graph vertex partition models for distributed computing due to their exibility in balancing loads and their performance in reducing communication cost [6, 16]. In this paper, we propose a simple yet e ective graph edge partitioning algorithm. In practice, our algorithm provides good partition quality (and better than similar state-of-the-art edge partition approaches, at least for power-law graphs) while maintaining low partition overhead. In theory, previous work [6] showed that an approximation guarantee of O(dmax log n log k ) apply to the graphs with m = (k 2 ) edges (k is the number of partitions). We further rigorously proved that this approximation guarantee hold for all graphs. We show how our edge partition model can be applied to parallel computing. We draw our example from GPU program locality enhancement and demonstrate that the graph edge partition model does not only apply

Journal

Proceedings of the ACM on Measurement and Analysis of Computing Systems – Association for Computing Machinery

Published: Jun 13, 2017

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