探索可扩展并行化,以编辑基于距离的动机搜索
IEEE/ACM transactions on computational biology and bioinformatics
|October 10, 2023
概括
本研究介绍了SPEMS,这是编辑基于距离的动机搜索 (EMS) 的并行算法. SPEMS提供可扩展的解决方案,用于在大型生物数据集中发现动机,在管理内存使用的同时实现显著的加快速度.
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
- 算法设计 算法设计
背景情况:
- 动机搜索对于从快速增长的数据集中提取生物学见解至关重要.
- 一般动机搜索的NP-hard性质需要高效的算法.
- 现有的方法难以满足现代生物数据分析的规模和速度需求.
研究的目的:
- 为编辑基于距离的动机搜索 (EMS) 开发可扩展和高效的并行算法.
- 引入实用的并行化策略,以优化速度和/或内存使用.
- 评估拟议方法在现实生物数据上的性能.
主要方法:
- 实现两个并行设计:recurSEMS (并行递归树) 和parEMS (基于线程).
- 开发SPEMS (对EMS的可扩展性敏感并行解决方案),以提供适应式并行化.
- 使用两个真实世界的DNA数据集进行评估:在48核机器上使用TRANSFAC和ChIP-seq.
主要成果:
- 与最先进的方法相比,SPEMS实现了高达10倍的几何平均速度提升,具有内存开销.
- 另外,SPEMS提供了2.2倍的几何平均速度提升,并可能节省内存.
- 解决器提供灵活的平行化,平衡性能和空间效率.
结论:
- SPEMS为编辑基于距离的动机搜索提供了一个实用且可扩展的解决方案.
- 该算法有效地解决了大型生物数据集所带来的计算挑战.
- 在模式发现方面,SPEMS表现出显著的性能改进,可适应不同的资源限制.
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