SW-演员:通过演员加速史密斯-沃特曼算法
Reza Rafati Bonab1, Ali Akbar Jamali1, Kyle Klenk1
1Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada.
Bioinformatics advances
|September 22, 2025
概括
史密斯-沃特曼 (SW) 算法是序列对齐的黄金标准,现在使用SW-actors更快. 这种并行实现可显著加快大型数据集的本地序列对齐.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 算法开发 算法开发
背景情况:
- 史密斯-沃特曼 (SW) 算法是局部序列对齐的黄金标准.
- 由于时间复杂性,SW的连续实现在处理大型生物数据集方面存在局限性.
研究的目的:
- 介绍SW-actors,这是SW算法的一个新的并行实现.
- 优化资源利用,提高局部序列对齐任务的效率.
主要方法:
- 利用并发计算的演员模型进行并行处理.
- 在相互调整和内部调整层面实施任务安排和管理.
- 在不同的数据集上,与最先进的工具 (Parasail,SeqAn,SWIPE) 进行了SW-actors的比较.
主要成果:
- 在现有实现中,SW-actors表现出明显的加快速度,从1.33x到2.49x不等.
- 与40个核心的串行执行相比,实现了高达22倍的速度.
- 对于较大的数据集,显示了一致的性能增长,对于中大规模对齐具有优势.
结论:
- SW-actors为本地序列对齐提供了一个高效和可扩展的解决方案.
- 并行实现克服了串行SW算法的局限性,用于大规模的生物信息任务.
- 演员模型为优化序列对齐中的并发计算提供了一个有效的框架.
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