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Performance Evaluation of R with Intel Xeon Phi Coprocessor

Yaakoub El-Khamra, Niall Gaffney, David Walling, Eric Wernert, Weijia Xu, Hui Zhang
Texas Advanced Computing Center, University of Texas at Austin, Austin, Texas USA
The First Workshop on Big Data Benchmarks, Performance Optimization, and Emerging hardware (BPOE 2013) in conjunction with 2013 IEEE International Conference on Big Data (IEEE Big Data 2013), 2013
@article{el2013performance,

   title={Performance Evaluation of R with Intel Xeon Phi Coprocessor},

   author={El-Khamra, Yaakoub and Gaffney, Niall and Walling, David and Wernert, Eric and Xu, Weijia and Zhang, Hui},

   year={2013}

}

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Over the years, R has been adopted as a major data analysis and mining tool in many domain fields. As Big Data overwhelms those fields, the computational needs and workload of existing R solutions increases significantly. With recent hardware and software developments, it is possible to enable massive parallelism with existing R solutions with little to no modification. In this paper, we evaluated approaches to speed up R computations with the utilization of the Intel Math Kernel Library and automatic offloading to Intel Xeon Phi SE10P Co-processor. The testing workload includes a popular R benchmark and a practical application in health informatics. There are up to five times speedup gains from using MKL with a 16 cores without modification to the existing code for certain computing tasks. Offloading to Phi co-processor further improves the performance. The performance gains through parallelization increases as the data size increases, a promising result for adopting R for big data problem in the future.
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