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

Previous Articles     Next Articles

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

[1] RODRIGUES A V,JORGE A,DUTRA I.Accelerating Recommender Systems using GPUs[C]∥ACM Symposium on Applied Computing.ACM,2015:879-884.
[2] GATES M,ANZT H,KURZAK J,et al.Accelerating Collaborative Filtering Using Concepts from High Performance Computing[C]∥2015 IEEE International Conference on Big Data (Big Data).IEEE,2015:667-676.
[3] PATEREK A.Improving regularized singular value decomposition for collaborative filtering[C]∥ACM International Con-ference on Knowledge Discovery and Data Mining.2007:39-42.
[4] ZHOU Y H,WILKINSON D,SCHREIBER R,et al.Large-scale Parallel Collaborative Filtering for the Netflix Prize[C]∥Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management.2008:337-348.
[5] YU H F,HSIEH C J,SI S,et al.Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems[C]∥2013 IEEE 13th International Conference on Data Mining(2012).2012:765-774.
[6] KOREN Y,BELL R,VOLINSKY C.Matrix Factorization Tech-niques for Recommender Systems[J].Computer,2009,2(8):30-37.
[7] ZHUANG Y,CHIN W S,JUAN W C,et al.A Fast Parallel SGD for Matrix Factorization in Shared Memory Systems[C]∥Proceedings of ACM Recommender Systems 2013.2013:249-256.

No related articles found!
Full text



No Suggested Reading articles found!