Conference Paper

Evolutionary placement of short sequence reads on multi-core architectures

DOI: 10.1109/AICCSA.2010.5586973 Conference: The 8th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2010, Hammamet, Tunisia, May 16-19, 2010
Source: DBLP


The application of high performance computing methods in bioinformatics becomes increasingly important because of the masses of data generated by novel short-read DNA sequencers. One important application of such short reads, is the analysis of microbial communities where the anonymous short reads need to be identified by sequence comparison to a set of reference sequences. This identification is required to analyze the microbial composition and biological diversity of the sample. We briefly introduce a new algorithm for evolutionary (phylogenetic) placement of short reads under the Maximum Likelihood criterion and implement it in RAxML. While this algorithm is significantly more accurate than plain pair-wise sequence comparison it can become highly compute-intensive when a typical number of 100,000 reads and more need to be placed into an existing phylogenetic tree. Therefore, we deploy multi-grain parallelism to improve parallel efficiency of this algorithm on 16-core and 32-core architectures. Via this multi-grain approach, we achieve parallel execution time improvements of 25% and super-linear speedups on 16 cores, as well as near-linear speedups and improvements exceeding 50% on 32-cores on two large real-world microbial datasets. Evolutionary placement of 100,000 reads into a tree with more than 4,000 taxa now only requires less than 2 hours of execution time on 32 cores.

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Available from: Alexandros Stamatakis, Feb 16, 2014
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    • "The EPA also is relatively straight-forward to parallelize by applying a multi-grain parallelization technique (Stamatakis et al., 2010). On a multi-core system with 32 cores and 64GB of main memory, we were able to place 100,627 QS in parallel into a RT with 4,874 taxa within 1.5 hours. "
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