Abstract
Non-negative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data compress and its ability of extracting highly-interpretable parts from data sets, and it has also been applied to various fields, such as recommendations, image analysis, and text clustering. However, as the size of the matrix increases, the processing speed of non-negative matrix factorization algorithm is very slow. To solve this problem, this paper proposes a parallel algorithm based on GPU for NMF in Spark platform, which makes full use of the advantages of in-memory computation mode and GPU Single-Instruction Multiple-data Streams mode. The new GPU-accelerated NMF on Spark platform is evaluated in a 4-nodes Spark heterogeneous cluster using Google Compute Engine by configuring each node a NVIDIA K80 GPU card, and experimental results indicate that it is competitive in terms of computational time against the existing solutions on a variety of matrix orders. It can achieve a high speed-up, and also can effectively deal with the non-negative decomposition of higher-order matrices, which greatly improves the computational efficiency.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China under grant no. 61602169 and 61702181, and the Natural Science Foundation of Hunan Province under grant no. 2018JJ2135 and 2018JJ3190, as well as the Scientific Research Fund of Hunan Provincial Education Department under grant no.16C0643.
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Tang, B., Kang, L., Xia, Y., Zhang, L. (2019). GPU-accelerated Large-Scale Non-negative Matrix Factorization Using Spark. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_13
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