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An efficient parallel collaborative filtering algorithm on multi-GPU platform

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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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Acknowledgments

This project is partially supported by grants from Natural Science Foundation of China #61202312/70621001/70921061. It is partially supported by DNSLAB at China Internet Network Information Center.

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Authors and Affiliations

  1. School of Computer and Control, University of Chinese Academy of Sciences, Beijing, China

    Zhongya Wang & Ying Liu

  2. Fictitious Economy and Data Science Research Center, Chinese Academy of Sciences, Beijing, China

    Ying Liu

  3. College of Science and Engineering, Idaho State University, Pocatello, ID, USA

    Steve Chiu

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  1. Zhongya Wang
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  2. Ying Liu
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  3. Steve Chiu
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Correspondence to Ying Liu.

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Wang, Z., Liu, Y. & Chiu, S. An efficient parallel collaborative filtering algorithm on multi-GPU platform. J Supercomput 72, 2080–2094 (2016). https://doi.org/10.1007/s11227-014-1333-4

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

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