6533b7d6fe1ef96bd12664c8

RESEARCH PRODUCT

parSRA: A framework for the parallel execution of short read aligners on compute clusters

Christian HundtBertil SchmidtJorge González-domínguez

subject

0301 basic medicineSource codeSpeedupGeneral Computer ScienceComputer sciencemedia_common.quotation_subjectParallel computingSupercomputerTheoretical Computer Science03 medical and health sciences030104 developmental biology0302 clinical medicine030220 oncology & carcinogenesisModeling and SimulationComputer clusterScalabilityFuse (electrical)Node (circuits)Partitioned global address spacemedia_common

description

The growth of next generation sequencing datasets poses as a challenge to the alignment of reads to reference genomes in terms of both accuracy and speed. In this work we present parSRA, a parallel framework to accelerate the execution of existing short read aligners on distributed-memory systems. parSRA can be used to parallelize a variety of short read alignment tools installed in the system without any modification to their source code. We show that our framework provides good scalability on a compute cluster for accelerating the popular BWA-MEM and Bowtie2 aligners. On average, it is able to accelerate sequence alignments on 16 64-core nodes (in total, 1024 cores) with speedup of 10.48 compared to the original multithreaded tools running with 64 threads on one node. It is also faster and more scalable than the pMap and BigBWA frameworks. Source code of parSRA in C++ and UPC++ running on Linux systems with support for FUSE is freely available at https://sourceforge.net/projects/parsra/.

yearjournalcountryeditionlanguage
2018-03-01Journal of Computational Science
https://doi.org/10.1016/j.jocs.2017.01.008