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Running scientific codes on amazon EC2: a performance analysis of five high-end instances
Keywords:
Cloud Computing, High Performance Computing, High Throughput Computing, Amazon EC2, OpenMPAbstract
Amazon Web Services (AWS) is a well-known public Infrastructure-as-a-Service (IaaS) provider whose Elastic Computing Cloud (EC2) o ering includes some instances, known as cluster instances, aimed at High-Performance Computing (HPC) applications. In previous work, authors have shown that the scalability of HPC communication-intensive applications does not bene t from using higher computational power cluster instances as much as it could be expected. Cost analysis recommends using lower computational power cluster instances unless high memory requirements preclude their use. Moreover, it has been observed that scalability is very poor when more than one instance is used due to network virtualization overhead. Based on those results, this paper gives more insight into the performance of running scienti c applications on the Amazon EC2 platform evaluating ve (of which two have been recently released) of the higher computational power instances in terms of single instance performance, intra-VM (Virtual Machine) scalability and cost-e ciency. The evaluation has been carried out using both an HPC benchmark suite and a real High-Troughput Computing (HTC) application.
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References
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[2] C. Evangelinos and C. N. Hill, “Cloud Computing for Parallel Scientific HPC Applications: Feasibility of Running Coupled Atmosphere-Ocean Climate Models on Amazon’s EC2,” in Proc. 1st Workshop on Cloud Computing and Its Applications (CCA’08), (Chicago, IL, USA), pp. 1–6, 2008.
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[4] Amazon Web Services LLC, “High Performance Computing on AWS.” http://aws.amazon.com/hpc-applications/. Last visited: Apr 2013.
[5] R. R. Expósito, G. L. Taboada, S. Ramos, J. Touriño, and R. Doallo, “Performance Analysis of HPC Applications in the Cloud,” Future Generation Computer Systems, vol. 29, no. 1, pp. 218 – 229, 2013.
[6] D. H. Bailey et al., “The NAS Parallel Benchmarks,” International Journal of High Performance Computing Applications, vol. 5, no. 3, pp. 63–73, 1991.
[7] D. Darriba, G. L. Taboada, R. Doallo, and D. Posada, “jModelTest 2: More Models, New Heuristics and Parallel Computing,” Nat Meth, vol. 9, p. 772, Aug. 2012.
[8] K. R. Jackson, L. Ramakrishnan, K. Muriki, S. Canon, S. Cholia, J. Shalf, H. J. Wasserman, and N. J. Wright, “Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud,” in Proc. 2nd IEEE Intl. Conference on Cloud Computing Technology and Science (Cloud-Com’10), (Indianapolis, USA), pp. 159–168, 2010.
[9] P. Luszczek, E. Meek, S. Moore, D. Terpstra, V. M. Weaver, and J. J. Dongarra, “Evaluation of the HPC Challenge Benchmarks in Virtualized Environments,” in Proc. 6th Workshop on Virtualization in High-Performance Cloud Computing (VHPC’11), (Bordeux, France), pp. 1–10, 2011.
[10] Y. Zhai, M. Liu, J. Zhai, X. Ma, and W. Chen, “Cloud Versus In-House Cluster: Evaluating Amazon Cluster Compute Instances for Running MPI Applications,” in Proc. 23th ACM/IEEE Conference on Supercomputing (SC’11, State of the Practice Reports), (Seattle, WA, USA), pp. 1–10, 2011.
[11] C. Sun, H. Nishimura, S. James, K. Song, K. Muriki, and Y. Qin, “HPC Cloud Applied to Lattice Optimization,” in Proc. 2nd Intl. Particle Accelerator Conference (IPAC’11), (San Sebastian, Spain), pp. 1767–1769, 2011.
[12] J. J. Rehr, F. D. Vila, J. P. Gardner, L. Svec, and M. Prange, “Scientific Computing in the Cloud,” Computing in Science and Engineering, vol. 12, no. 3, pp. 34–43, 2010.
[13] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield, “Xen and the Art of Virtualization,” in Proc. 19th ACM Symposium on Operating Systems Principles (SOSP’03), (Bolton Landing, NY, USA), pp. 164–177, 2003.
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Published
2018-04-13
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[1]
“Running scientific codes on amazon EC2: a performance analysis of five high-end instances”, JCS&T, vol. 13, no. 03, pp. 153–159, Apr. 2018, Accessed: Jun. 18, 2025. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/597
