Massively Parallel Sequence Analysis with Hidden Markov Models
نویسنده
چکیده
Molecular biologists use Hidden Markov Models (HMMs) as a popular tool to statistically describe protein sequence families. This statistical description can then be used for sensitive and selective database scanning. Even though efficient dynamic programming algorithms exist for the problem, the required scanning time is still very high, and because of the exponential database growth finding fast solutions is of high importance to research in this area. In this paper we illustrate how massive parallelism can be used for efficient sequence analysis using HMMs. We present two new techniques to parallelize the dynamic programming calculation: “diagonal-by-diagonal” and “row-by-row”. This leads to significant runtime savings on our hybrid parallel system based on commodity components to gain high performance at low cost. The architecture is built around a coarse-grained PC-cluster linked by a high-speed network and fine-grained SIMD processor arrays connected to each node.
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تاریخ انتشار 2002