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Issue No.02 - March-April (2012 vol.29)
pp: 52-58
Radu Prodan , University of Innsbruck
Michael Sperk , University of Innsbruck
Simon Ostermann , University of Innsbruck
An experimental approach employs the Google App Engine (GAE) for high-performance parallel computing. A generic master-slave framework enables fast prototyping and integration of parallel algorithms that are transparently scheduled and executed on the Google cloud infrastructure. Compared to Amazon Elastic Compute Cloud (EC2), GAE offers lower resource-provisioning overhead and is cheaper for jobs shorter than one hour. Experiments demonstrated good scalability of a Monte Carlo simulation algorithm. Although this approach produced important speedup, two main obstacles limited its performance: middleware overhead and resource quotas.
cloud computing, high-performance computing, performance analysis, Google App Engine, GAE, Amazon Elastic Compute Cloud, Amazon EC2, software engineering
Radu Prodan, Michael Sperk, Simon Ostermann, "Evaluating High-Performance Computing on Google App Engine", IEEE Software, vol.29, no. 2, pp. 52-58, March-April 2012, doi:10.1109/MS.2011.131
1. D. Sanderson, Programming Google App Engine, O'Reilly Media, 2009.
2. A. Iosup et al., "Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing," IEEE Trans. Parallel and Distributed Systems, vol. 22, no. 6, 2011, pp. 931–945.
3. M. Sperk, "Scientific Computing in the Cloud with Google App Engine," master's thesis, Faculty of Mathematics, Computer Science, and Physics, Univ. of Innsbruck, 2011;
4. J.L. Gustafson, "Reevaluating Amdahl's Law," Comm. ACM, vol. 31, no. 5, 1988, pp. 532–533.
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