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A Simple Yet Effective Balanced Edge Partition Model for Parallel Computing

Authors:

Pranav Chaudhari,

Mario SzegedyAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 45, Issue 1

Page 6

https://doi.org/10.1145/3143314.3078520

Published: 05 June 2017 Publication History

Abstract

Graph edge partition models have recently become an appealing alternative to graph vertex partition models for distributed computing due to both their flexibility in balancing loads and their performance in reducing communication cost.

In this paper, we propose a simple yet effective graph edge partitioning algorithm. In practice, our algorithm provides good partition quality while maintaining low partition overhead. It also outperforms similar state-of-the-art edge partition approaches, especially for power-law graphs. In theory, previous work showed that an approximation guarantee of O(d_max√(log n log k)) apply to the graphs with m=Ω(k²) edges (n is the number of vertices, and k is the number of partitions). We further rigorously proved that this approximation guarantee hold for all graphs.

We also demonstrate the applicability of the proposed edge partition algorithm in real parallel computing systems. We draw our example from GPU program locality enhancement and demonstrate that the graph edge partition model does not only apply to distributed computing with many computer nodes, but also to parallel computing in a single computer node with a many-core processor.

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A Simple Yet Effective Balanced Edge Partition Model for Parallel Computing

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 45, Issue 1

Performance evaluation review

June 2017

70 pages

ISSN:0163-5999

DOI:10.1145/3143314

Editor:
Nidhi Hegde
Bell Labs, Nokia

Issue’s Table of Contents

SIGMETRICS '17 Abstracts: Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems
June 2017
84 pages
ISBN:9781450350327
DOI:10.1145/3078505
General Chairs:
Bruce Hajek
University of Illinois
,
Sewoong Oh
University of Illinois
,
Program Chairs:
Augustin Chaintreau
Columbia University
,
Leana Golubchik
University of Southern California
,
Zhi-Li Zhang
University of Minnesota

Copyright © 2017 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 05 June 2017

Published in SIGMETRICS Volume 45, Issue 1

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https://doi.org/10.1109/CNS59707.2023.10288754
Gan XZhang YZeng RLiu JWang RLi TChen LLu K(2022)XTree: Traversal-Based Partitioning for Extreme-Scale Graph Processing on Supercomputers2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00199(2046-2059)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00199
Song DZhang FLu MYang SHuang H(2021)DTransE: Distributed Translating Embedding for Knowledge GraphIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.306644232:10(2509-2523)Online publication date: 1-Oct-2021
https://doi.org/10.1109/TPDS.2021.3066442
Lu MLiao LZhang FSong D(2020)Exploration of TransE in a Distributed Environment2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS47774.2020.00190(1173-1174)Online publication date: Nov-2020
https://doi.org/10.1109/ICDCS47774.2020.00190
Mabrouk LHuet SHouzet DBelkouch SHamzaoui AZennayi Y(2019)Efficient adaptive load balancing approach for compressive background subtraction algorithm on heterogeneous CPU–GPU platformsJournal of Real-Time Image Processing10.1007/s11554-019-00916-4Online publication date: 26-Sep-2019
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Grossman SLitz HKozyrakis CKrall AGross T(2018)Making pull-based graph processing performantProceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming10.1145/3178487.3178506(246-260)Online publication date: 10-Feb-2018
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Schulz CStrash D(2018)Graph Partitioning: Formulations and Applications to Big DataEncyclopedia of Big Data Technologies10.1007/978-3-319-63962-8_312-2(1-7)Online publication date: 20-Mar-2018
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