Performance Engineering for Scientific Computing with R

Hui Zhang

doi:doi:10.11648/j.ijdst.20180402.11

| Peer-Reviewed

Performance Engineering for Scientific Computing with R

Hui Zhang

Published in International Journal on Data Science and Technology (Volume 4, Issue 2)

Received: 25 June 2018 Published: 26 June 2018

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

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.

Published in	International Journal on Data Science and Technology (Volume 4, Issue 2)
DOI	10.11648/j.ijdst.20180402.11
Page(s)	42-48
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Performance Evaluation, R, Intel Xeon Phi, Multi-Core Computing, GPGPU

References

[1]	Accelerating the intel math kernel library, 2007. M. Intel. Intel math kernel library, 2007.
[2]	A hardware accelerator for the Intel Math Kernel. J. L. Gustafson and B. S. Greer. ClearSpeed whitepaper.
[3]	Y. El-Khamra, N. Gaffney, D. Walling, E. Wernert, W. Xu, and H. Zhang. Performance evaluation of r with intel xeon phicoprocessor. In Big Data, 2013 IEEE International Conference on, pages 23–30. IEEE, 2013.
[4]	Hui Zhang, Sidharth Thakur, and Andrew J. Hanson. Haptic exploration of mathematical knots. In ISVC (1), pages 745–756, 2007.
[5]	Lin Jing, Xipei Huang, Yiwen Zhong, Yin Wu, and Hui Zhang. Python based 4d visualization environment. International Journal of Advancements in Computing Technology, 4 (16):460–469, September 2012.
[6]	Hui Zhang, Jianguang Weng, and Andrew J. Hanson. A pseudo-haptic knot diagram interface. In Proc. SPIE, volume 7868, pages 786807–786807–14, 2011.
[7]	Guangchen Ruan and Hui Zhang. Conquering Big Data with High Performance Computing, chapter Large-Scale Multimodal Data Exploration with Human in the Loop. Springer International Publishing, Springer International Publishing Switzerland, 2016.
[8]	Jian Zou and Hui Zhang. Conquering Big Data with High Performance Computing, chapter High-Frequency Financial Analysis through High Performance Computing. Springer International Publishing, Springer International Publishing Switzerland, 2016.
[9]	Weijia Xu, Ruizhu Huang, and Hui Zhang. Conquering Big Data with High Performance Computing, chapter Empowering R with High Performance Computing Resources for Big Data Analytics. Springer International Publishing, Springer International Publishing Switzerland, 2016.
[10]	Hui Zhang, Huian Li, Michael J. Boyles, Robert Henschel, Eduardo Kazuo Kohara, and Masatoshi Ando. Exploiting hpc resources for the 3d-time series analysis of caries lesion activity. In Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the Campus and Beyond, XSEDE ’12, pages 19:1–19:8, New York, NY, USA, 2012. ACM.
[11]	Hui Zhang, Michael J. Boyles, Guangchen Ruan, Huian Li, Hongwei Shen, and Masatoshi Ando. Xsede-enabled highthroughput lesion activity assessment. In Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery, XSEDE ’13, pages 10:1–10:8, New York, NY, USA, 2013. ACM.
[12]	Hui Zhang, Jianguang Weng, and Guangchen Ruan. Visualizing 2-dimensional manifolds with curve handles in 4d. IEEE Transactions on Visualization and Computer Graphics, 20 (12):2575–2584, Dec 2014.
[13]	Riqing Chen and Hui Zhang. Large-scale 3D Reconstruction with an R-based Analysis Workflow. In Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT '17). ACM, New York, NY, USA.
[14]	Hui Zhang, Yiwen. Zhong and Juan Lin, Divide-and-conquer strategies for large-scale simulations in R, 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 3517-3523.

Cite This Article

Plain Text BibTeX RIS

APA Style

Hui Zhang. (2018). Performance Engineering for Scientific Computing with R. International Journal on Data Science and Technology, 4(2), 42-48. https://doi.org/10.11648/j.ijdst.20180402.11

Copy | Download

ACS Style

Hui Zhang. Performance Engineering for Scientific Computing with R. Int. J. Data Sci. Technol. 2018, 4(2), 42-48. doi: 10.11648/j.ijdst.20180402.11

Copy | Download

AMA Style

Hui Zhang. Performance Engineering for Scientific Computing with R. Int J Data Sci Technol. 2018;4(2):42-48. doi: 10.11648/j.ijdst.20180402.11

Copy | Download

@article{10.11648/j.ijdst.20180402.11,
  author = {Hui Zhang},
  title = {Performance Engineering for Scientific Computing with R},
  journal = {International Journal on Data Science and Technology},
  volume = {4},
  number = {2},
  pages = {42-48},
  doi = {10.11648/j.ijdst.20180402.11},
  url = {https://doi.org/10.11648/j.ijdst.20180402.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180402.11},
  abstract = {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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Performance Engineering for Scientific Computing with R
AU  - Hui Zhang
Y1  - 2018/06/26
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdst.20180402.11
DO  - 10.11648/j.ijdst.20180402.11
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 42
EP  - 48
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20180402.11
AB  - 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.
VL  - 4
IS  - 2
ER  -

Copy | Download

Author Information

Hui Zhang

Computer Engineering and Computer Science Department, University of Louisville, Louisville, USA

Download PDF

Submit an Article

Sections

Plain Text BibTeX RIS

APA Style

Hui Zhang. (2018). Performance Engineering for Scientific Computing with R. International Journal on Data Science and Technology, 4(2), 42-48. https://doi.org/10.11648/j.ijdst.20180402.11

Copy | Download

ACS Style

Hui Zhang. Performance Engineering for Scientific Computing with R. Int. J. Data Sci. Technol. 2018, 4(2), 42-48. doi: 10.11648/j.ijdst.20180402.11

Copy | Download

AMA Style

Hui Zhang. Performance Engineering for Scientific Computing with R. Int J Data Sci Technol. 2018;4(2):42-48. doi: 10.11648/j.ijdst.20180402.11

Copy | Download

@article{10.11648/j.ijdst.20180402.11,
  author = {Hui Zhang},
  title = {Performance Engineering for Scientific Computing with R},
  journal = {International Journal on Data Science and Technology},
  volume = {4},
  number = {2},
  pages = {42-48},
  doi = {10.11648/j.ijdst.20180402.11},
  url = {https://doi.org/10.11648/j.ijdst.20180402.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180402.11},
  abstract = {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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Performance Engineering for Scientific Computing with R
AU  - Hui Zhang
Y1  - 2018/06/26
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdst.20180402.11
DO  - 10.11648/j.ijdst.20180402.11
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 42
EP  - 48
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20180402.11
AB  - 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.
VL  - 4
IS  - 2
ER  -

Copy | Download