research-article

Cache-conscious graph collaborative filtering on multi-socket multicore systems

Authors:

Ching-Yung Lin,

Hsien-Hsin S. LeeAuthors Info & Claims

CF '14: Proceedings of the 11th ACM Conference on Computing Frontiers

Article No.: 32, Pages 1 - 10

https://doi.org/10.1145/2597917.2597935

Published: 20 May 2014 Publication History

Abstract

Recommendation systems using graph collaborative filtering often require responses in real time and high throughput. Therefore, besides recommendation accuracy, it is critical to study high performance concurrent collaborative filtering on modern platforms. To achieve high performance, we study the graph data locality characteristics of collaborative filtering. Our experiments demonstrate that although an individual graph traversal exhibits poor data locality, multiple queries have a tendency of sharing their data footprints, especially in the case of queries with neighboring root vertices. Such characteristics lead to both inter- and intra-thread data locality, which can be utilized to significantly improve collaborative filtering performance.

Based on these observations, we present a cache-conscious system for collaborative filtering on modern multi-socket multicore platforms. In this system, we propose a cache-conscious query scheduling technique and an in-memory graph representation, and to maximize cache performance and minimize cross-core/socket communication overhead, we address both inter- and intra-thread data locality. To address the workload balancing issue, this study introduces a dynamic work-stealing mechanism to explore the tradeoff between workload balancing and cache-consciousness.

The proposed system was evaluated on a Power7+ system against the IBM Knowledge Repository graph dataset. The results demonstrated both good scalability and throughput. Compared with the basic system that does not perform cache-conscious scheduling, inter-thread scheduling improves throughput by up to 18%. Intra-thread scheduling can further improve throughput by as much as 22%. By enabling dynamic work-stealing, the proposed technique balances workloads across all threads with a low standard deviation of the per-thread processing time.

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Cited By

Nai LXia YTanase IKim H(2017)Exploring big graph computing — An empirical study from architectural perspectiveJournal of Parallel and Distributed Computing10.1016/j.jpdc.2016.07.006108(122-137)Online publication date: Oct-2017
https://doi.org/10.1016/j.jpdc.2016.07.006
Wang WDavidson JSoffa M(2016)Predicting the memory bandwidth and optimal core allocations for multi-threaded applications on large-scale NUMA machines2016 IEEE International Symposium on High Performance Computer Architecture (HPCA)10.1109/HPCA.2016.7446083(419-431)Online publication date: Mar-2016
https://doi.org/10.1109/HPCA.2016.7446083
Nai LXia YTanase IKim HLin CKern JVetter J(2015)GraphBIGProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/2807591.2807626(1-12)Online publication date: 15-Nov-2015
https://dl.acm.org/doi/10.1145/2807591.2807626

Index Terms

Cache-conscious graph collaborative filtering on multi-socket multicore systems

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Published In

cover image ACM Conferences

CF '14: Proceedings of the 11th ACM Conference on Computing Frontiers

May 2014

305 pages

ISBN:9781450328708

DOI:10.1145/2597917

General Chair:
Pedro Trancoso
University of Cyprus, CY
,
Program Chairs:
Diana Franklin
University of California at Santa Barbara
,
Sally A. McKee
Chalmers University of Technology, SE

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 May 2014

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May 20 - 22, 2014

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CF '14 Paper Acceptance Rate 28 of 62 submissions, 45%;

Overall Acceptance Rate 273 of 785 submissions, 35%

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Cited By

Nai LXia YTanase IKim H(2017)Exploring big graph computing — An empirical study from architectural perspectiveJournal of Parallel and Distributed Computing10.1016/j.jpdc.2016.07.006108(122-137)Online publication date: Oct-2017
https://doi.org/10.1016/j.jpdc.2016.07.006
Wang WDavidson JSoffa M(2016)Predicting the memory bandwidth and optimal core allocations for multi-threaded applications on large-scale NUMA machines2016 IEEE International Symposium on High Performance Computer Architecture (HPCA)10.1109/HPCA.2016.7446083(419-431)Online publication date: Mar-2016
https://doi.org/10.1109/HPCA.2016.7446083
Nai LXia YTanase IKim HLin CKern JVetter J(2015)GraphBIGProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/2807591.2807626(1-12)Online publication date: 15-Nov-2015
https://dl.acm.org/doi/10.1145/2807591.2807626

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