Abstract
As a convenient high performance computation system, cloud system is more and more popularly used in the field of bioinformatics. We develop and examine several hybrid computation models for high performance biological sequence alignment on a cloud system. In our practice, Smith-Waterman and CloudBurst alignment algorithms are evaluated for performance with the computation models, which are built from combinations of current technologies including Hadoop, SHadoop and Fair Scheduler, on the Amazon Elastic Compute Cloud (EC2) system. In our experiment with relatively small data sets, the computation model with SHadoop showed the best performance for both Smith-Waterman and CloudBurst algorithms, i.e., speedup of 1.86× and 1.19×, respectively, over the baseline model (with Hadoop). For relatively large data sets, the computation model with SHadoop showed the best performance with Smith-Waterman algorithm (1.03×) and the computation model with SHadoop plus Fair Scheduler showed the best performance with CloudBurst algorithm (1.15×).
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Job, T., Park, J.H. (2015). Hybrid Computation Models for High Performance Biological Sequence Alignment on a Cloud System. In: Kim, H., Amouzegar, M., Ao, Sl. (eds) Transactions on Engineering Technologies. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7236-5_26
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DOI: https://doi.org/10.1007/978-94-017-7236-5_26
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