International Journal on Data Science and Technology
Volume 4, Issue 2, June 2018, Pages: 42-48
Received: Jun. 25, 2018;
Published: Jun. 26, 2018
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Hui Zhang, Computer Engineering and Computer Science Department, University of Louisville, Louisville, USA
R has been adopted as a popular data analysis and mining tool in many domain fields over the past decade. 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, three different approaches are evaluated to speed up R computations with the utilization of the multiple cores, the Intel Xeon Phi SE10P Co-processor, and the general purpose graphic processing unit (GPGPU). Performance engineering and evaluation efforts in this study are based on a popular R benchmark script. The paper presents preliminary results on running R-benchmark with the above packages and hardware technology combinations.
Performance Engineering for Scientific Computing with R, International Journal on Data Science and Technology.
Vol. 4, No. 2,
2018, pp. 42-48.
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