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ABSTRACT: Today's emerging architectures have higher levels of parallelism incorporated within a processor. They require efficient strategies to extract the performance they have to offer. In our work, we develop architecture-aware parallel strategies to perform various kinds of pairwise computations - pairwise genomic alignments, and scheduling large number of general pairwise computations with applications to computational systems biology and materials science. We present our schemes in the context of the IBM Cell BE, an example of a heterogeneous multicore, but are nevertheless applicable to any similar architecture, as well as general multicores with our strategies being cache-aware.
Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on; 05/2010
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ABSTRACT: Genomic alignments, as a means to uncover evolutionary relationships among organisms, are a fundamental tool in computational biology. There is considerable recent interest in using the Cell Broadband Engine, a heterogeneous multicore chip that provides high performance, for biological applications. However, work in genomic alignments so far has been limited to computing optimal alignment scores using quadratic space for the basic global/local alignment problem. In this paper, we present a comprehensive study of developing alignment algorithms on the Cell, exploiting its thread and data level parallelism features. First, we develop a parallel implementation on the Cell that computes optimal alignments and adopts Hirschberg's linear space technique. The former is essential, as merely computing optimal alignment scores is not useful, while the latter is needed to permit alignments of longer sequences. We then present Cell implementations of two advanced alignment techniques-spliced alignments and syntenic alignments. Spliced alignments are useful in aligning mRNA sequences with corresponding genomic sequences to uncover the gene structure. Syntenic alignments are used to discover conserved exons and other sequences between long genomic sequences from different organisms. We present experimental results for these three types of alignments on 16 Synergistic Processing Elements of the IBM QS20 dual-Cell blade system.
IEEE Transactions on Parallel and Distributed Systems 12/2009; · 1.40 Impact Factor
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ABSTRACT: Sequence alignment and its many variants are a fundamental tool in computational biology. There is considerable recent interest in using the cell broadband engine, a heterogenous multi-core chip that provides high performance, for biological applications. However, work so far has been limited to computing optimal alignment scores using quadratic space under the basic global/local alignment algorithm. In this paper, we present a comprehensive study of developing sequence alignment algorithms on the Cell exploiting its thread and data level parallelism features. First, we develop a cell implementation that computes optimal alignments and adopts Hirschberg's linear space technique. The former is essential as merely computing optimal alignment scores is not useful while the latter is needed to permit alignments of longer sequences. We then present cell implementations of two advanced alignment techniques - spliced alignments and syntenic alignments. In a spliced alignment, consecutive non-overlapping portions of a sequence align with ordered non-overlapping, but non-consecutive portions of another sequence. Spliced alignments are useful in aligning mRNA sequences with corresponding genomic sequences to uncover gene structure. Syntenic alignments are used to discover conserved exons and other sequences between long genomic sequences from different organisms. We present experimental results for these three types of alignments on the Cell BE and report speedups of about 4 on six SPUs on the Playstation 3, when compared to the respective best serial algorithms on the Cell BE and the Pentium 4 processor.
Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on; 05/2008
