AzureBlast: A Case Study of Developing Science Applications on the Cloud

Cloud computing has emerged as a new approach to large scale computing and is attracting a lot of attention from the scientific and research computing communities. Despite its growing popularity, it is still unclear just how well the cloud model of computation will serve scientific applications. In this paper we analyze the applicability of cloud to the sciences by investigating an implementation of a well known and computationally intensive algorithm called BLAST. BLAST is a very popular life sciences algorithm used commonly in bioinformatics research. The BLAST algorithm makes an excellent case study because it is both crucial to many life science applications and its characteristics are representative of many applications important to data intensive scientific research. In our paper we introduce a methodology that we use to study the applicability of cloud platforms to scientific computing and analyze the results from our study. In particular we examine the best practices of handling the large scale parallelism, large volumes of data, and how to make best use of the unique elastic scalability that cloud platforms provide. While we carry out our performance evaluation on Microsoft’s Windows Azure the results readily generalize to other cloud platforms.

In  Proceedings of the 1st Workshop on Scientific Cloud Computing (Science Cloud 2010)

Publisher  Association for Computing Machinery, Inc.
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