article

An efficient parallel collaborative filtering algorithm on multi-GPU platform

Authors:

Steve ChiuAuthors Info & Claims

The Journal of Supercomputing, Volume 72, Issue 6

Pages 2080 - 2094

https://doi.org/10.1007/s11227-014-1333-4

Published: 01 June 2016 Publication History

Abstract

Collaborative filtering (CF) is one of the essential algorithms in recommendation system. As the size of the data in real applications is huge, usually at the magnitude of Petabytes, parallel computing technique is required to accelerate the computation. Due to GPU's tremendous computing capability, it has emerged as the co-processor of the CPU to achieve a high overall throughput. In this paper, we identify the computation kernel, similarity matrix calculation. Then, we present a CUDA multithread model, where the data elements are processed in a data-parallel fashion. A workload partitioning scheme is proposed to balance the workload distributed to different GPUs. In the experiments, our CUDA-enabled CF algorithm significantly outperforms the serial CF workstation, achieving up to 3,691$$\times $$ speedup using four Tesla K10 graphics cards. It also shows good scalability when varying the number of users, the number of items and the number of GPUs.

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Cited By

Stokfiszewski KWieloch KYatsymirskyy M(2022)An efficient implementation of one-dimensional discrete wavelet transform algorithms for GPU architecturesThe Journal of Supercomputing10.1007/s11227-022-04331-878:9(11539-11563)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11227-022-04331-8
Tang BKang LZhang LGuo FHe H(2021)Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark PlatformScientific Programming10.1155/2021/88411332021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8841133
Amer AAbdalla HNguyen L(2021)Enhancing recommendation systems performance using highly-effective similarity measures▪Knowledge-Based Systems10.1016/j.knosys.2021.106842217:COnline publication date: 6-Apr-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.106842
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Published In

cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 72, Issue 6

June 2016

378 pages

ISSN:0920-8542

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Copyright © Copyright © 2016 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2016

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Cited By

Stokfiszewski KWieloch KYatsymirskyy M(2022)An efficient implementation of one-dimensional discrete wavelet transform algorithms for GPU architecturesThe Journal of Supercomputing10.1007/s11227-022-04331-878:9(11539-11563)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11227-022-04331-8
Tang BKang LZhang LGuo FHe H(2021)Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark PlatformScientific Programming10.1155/2021/88411332021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8841133
Amer AAbdalla HNguyen L(2021)Enhancing recommendation systems performance using highly-effective similarity measures▪Knowledge-Based Systems10.1016/j.knosys.2021.106842217:COnline publication date: 6-Apr-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.106842
Zhu LLi HFeng Y(2019)Research on big data mining based on improved parallel collaborative filtering algorithmCluster Computing10.1007/s10586-018-2209-922:2(3595-3604)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10586-018-2209-9
Wei XHu MPeng TJiang MWang ZQin X(2019)PRODA: improving parallel programs on GPUs through dependency analysisCluster Computing10.1007/s10586-017-1295-422:1(2129-2144)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s10586-017-1295-4
Zhou SKannan RMin YPrasanna VAnderson JBazargan K(2018)FASTCFProceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays10.1145/3174243.3174252(259-268)Online publication date: 15-Feb-2018
https://dl.acm.org/doi/10.1145/3174243.3174252
Hewanadungodage CXia YLee J(2017)A GPU-oriented online recommendation algorithm for efficient processing of time-varying continuous data streamsKnowledge and Information Systems10.1007/s10115-016-0967-353:3(637-670)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10115-016-0967-3

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