NDLI: DSA: Scalable Distributed Sequence Alignment System Using SIMD Instructions

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DSA: Scalable Distributed Sequence Alignment System Using SIMD Instructions

Content Provider	ACM Digital Library
Author	Li, Changlong Zhou, Xuehai Zhuang, Hang Zhou, Jinhong Wang, Qingfeng Xu, Bo Wang, Jiali
Abstract	Sequence alignment algorithms are a basic and critical component of many bioinformatics fields. With rapid development of sequencing technology, the fast growing reference database volumes and longer length of query sequence become new challenges for sequence alignment. However, the algorithms have prohibitively high time and space complexity. In this paper, we present DSA, a scalable distributed sequence alignment system that employs Apache Spark to process sequences data in a horizontally scalable distributed environment, and leverages data parallel strategy based on Single Instruction Multiple Data (SIMD) instruction to parallelize the algorithms in each core of worker node. The experimental results demonstrate that 1) DSA has outstanding performance and achieves up to 201x speedup over SparkSW. 2) DSA has excellent scalability and achieves near linear speedup when increasing the number of nodes in cluster.
Starting Page	758
Ending Page	761
Page Count	4
File Format	PDF
ISBN	9781509066100
DOI	10.1109/CCGRID.2017.74
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-14
Access Restriction	Subscribed
Subject Keyword	Alluxio Scalability Distributed sequence alignment Apache spark Simd instruction
Content Type	Text
Resource Type	Article

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