基于fm索引的基因组模式搜索的多fpga实现

IF 0.6 4区 计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS IEICE Transactions on Information and Systems Pub Date : 2023-11-01 DOI:10.1587/transinf.2022edp7230
Ullah IMDAD, Akram BEN AHMED, Kazuei HIRONAKA, Kensuke IIZUKA, Hideharu AMANO
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引用次数: 0

摘要

由多个FPGA板组成的FPGA集群近年来越来越受到关注。大规模并行处理与独立的异构FPGA集群与SoC风格的FPGA和中等规模的FPGA是有希望的成本效益效益。在这里,我们提出了一个基于FiC和M-KUBOS集群的异构FPGA集群。FiC由多个电路板组成,安装中等规模的赛灵思fpga和dram,它们与高速串行链路紧密耦合。此外,M-KUBOS板连接FiC,确保高IO数据传输带宽。作为大规模并行处理的一个例子,这里我们实现基因组模式搜索。新一代测序(NGS)技术以其高速、可扩展和大通量的特点,为生物系统相关研究带来了革命性的变化。为了分析基因组数据,使用短读作图技术,其中短脱氧核糖核酸(DNA)序列相对于已知参考序列作图。虽然有几种模式匹配技术可用,但基于fm索引的模式搜索非常适合此任务,因为它可以最快地从已知索引进行映射。由于可以对不同的数据并行进行匹配,因此可以应用数据分布、并行执行和结果采集的大规模并行计算。我们还实现了一种数据压缩方法,该方法将数据大小减少了大约10倍。我们发现一个M-KUBOS板匹配4个FiC板,一个包含6个M-KUBOS板和24个FiC板的系统比基于软件的实现快30倍。
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A Multi-FPGA Implementation of FM-Index Based Genomic Pattern Search
FPGA clusters that consist of multiple FPGA boards have been gaining interest in recent times. Massively parallel processing with a stand-alone heterogeneous FPGA cluster with SoC- style FPGAs and mid-scale FPGAs is promising with cost-performance benefit. Here, we propose such a heterogeneous FPGA cluster with FiC and M-KUBOS cluster. FiC consists of multiple boards, mounting middle scale Xilinx's FPGAs and DRAMs, which are tightly coupled with high-speed serial links. In addition, M-KUBOS boards are connected to FiC for ensuring high IO data transfer bandwidth. As an example of massively parallel processing, here we implement genomic pattern search. Next-generation sequencing (NGS) technology has revolutionized biological system related research by its high-speed, scalable and massive throughput. To analyze the genomic data, short read mapping technique is used where short Deoxyribonucleic acid (DNA) sequences are mapped relative to a known reference sequence. Although several pattern matching techniques are available, FM-index based pattern search is perfectly suitable for this task due to the fastest mapping from known indices. Since matching can be done in parallel for different data, the massively parallel computing which distributes data, executes in parallel and gathers the results can be applied. We also implement a data compression method where about 10 times reduction in data size is achieved. We found that a M-KUBOS board matches four FiC boards, and a system with six M-KUBOS boards and 24 FiC boards achieved 30 times faster than the software based implementation.
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来源期刊
IEICE Transactions on Information and Systems
IEICE Transactions on Information and Systems 工程技术-计算机:软件工程
CiteScore
1.80
自引率
0.00%
发文量
238
审稿时长
5.0 months
期刊介绍: Published by The Institute of Electronics, Information and Communication Engineers Subject Area: Mathematics Physics Biology, Life Sciences and Basic Medicine General Medicine, Social Medicine, and Nursing Sciences Clinical Medicine Engineering in General Nanosciences and Materials Sciences Mechanical Engineering Electrical and Electronic Engineering Information Sciences Economics, Business & Management Psychology, Education.
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