Computer Science ›› 2017, Vol. 44 ›› Issue (10): 71-74.doi: 10.11896/j.issn.1002-137X.2017.10.013

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Implementation and Performance Evaluation of Recommender Algorithms Based on Multi-/Many-core Platforms

CHEN Jing, FANG Jian-bin, TANG Tao and YANG Can-qun   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In this paper,we designed and implemented two typical recommender algorithms,alternating least squares and cyclic coordinate descent in openCL.Then we evaluated them on Intel CPUs,NVIDIA GPUs and Intel MIC,and investigated the performance impacting factors: potential feature dimension and the number of thread.Meanwhile,we compared the OpenCL implementation with that of CUDA and OpenMP.Our experimental results show that in the same condition,CCD converges faster and performs more steadily,but is more time-consuming than ALS.We also observed that the performance based on OpenCL is better than CUDA and OpenMP when running on the same platform:the training time on GPU is slightly faster than that of the CUDA implementation (1.03x for CCD and 1.2x for ALS),and the training time on CPU is 1.6~1.7 times less than that of the OpenMP implementation with 16 threads.When running the OpenCL implementation on different platforms,we noticed that CPU performs better than both the GPU and the MIC.

Key words: Recommender system,OpenCL,ALS,CCD

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