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BIMSA:使用内存处理加速长序列对齐

Alejandro Alonso-Marín^1,2,3, Ivan Fernandez^1,4, Quim Aguado-Puig^1,3,5

¹Department of Computer Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain.

Bioinformatics (Oxford, England)

|October 21, 2024

概括

本研究介绍了BIMSA,这是一个用于更快的序列对齐的内存处理设计. 通过减少序列分析算法的数据移动瓶,BIMSA加速了基因组学研究.

科学领域:

基因组学就是基因组学.
计算机架构计算机架构
生物信息学是一种生物信息学.

背景情况:

测序技术的进步需要高效的测序分析工具.
序列对齐是基因组学管道中的关键,但往往是性能瓶的步骤.
经典算法由于内存和时间复杂性而难以处理大型数据集.

研究的目的:

开发一个处理内存 (PIM) 设计,以加速序列对齐.
在PIM架构上实现和优化双向波面对齐 (BiWFA) 算法.
克服现有的PIM实施方案对序列对齐的局限性.

主要方法:

在UPMEM PIM架构上设计和实施BIMSA (双向内存序列对齐).
集成的硬件意识优化,专门为BiWFA.
与最先进的PIM和CPU实现相比,评估了性能.

主要成果:

BIMSA实现了显著的加快速度:比支持PIM的算法加快了22.24倍,比CPU实现加快了5.84倍.
支持最多100K个基数的对齐序列,超过目前的PIM功能.
证明了与内存计算单元的线性可扩展性,承诺未来的性能增长.

结论:

BIMSA有效地通过使用内存处理加速序列对齐.
该设计为基因组学和医疗保健研究提供了实质性的性能改进.
在生物信息学中,BIMSA的可扩展性为下一代PIM架构铺平了道路.

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