Abstract
R is a widely-used statistical programming language in the data science community. However, in the big data era, R faces the challenges from large scale data analysis tasks. It lacks the ability of distributed linear algebra computation in its local interactive shell. In this paper, we propose iPLAR, a system that runs in the interactive R environment, wraps the high performance parallel linear algebra library, and provides a group of easy-to-use interfaces. iPLAR adopts the client-server model to uncouple the interactive shell from the ScaLAPACK/MPI distributed computing backend. In addition, it provides R users with a group of parallel-detail-transparent interfaces that are similar to the native R linear algebra interfaces. We evaluate the efficiency of iPLAR with representative basic matrix operations and two widely-used machine learning algorithms. Experimental results show that iPLAR achieves the near-linear data scalability and enhances the interactive processing capability of R to large problem scales.
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This work is funded in part by China NSF Grants (No. 61572250), Jiangsu Province Industry Support Program (BE2014131) and China NSF Grants (No. 61223003).
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Wang, Z., Fan, S., Gu, R., Yuan, C., Huang, Y. (2015). iPLAR: Towards Interactive Programming with Parallel Linear Algebra in R. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9531. Springer, Cham. https://doi.org/10.1007/978-3-319-27140-8_8
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DOI: https://doi.org/10.1007/978-3-319-27140-8_8
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