用于同源蛋白质发现的并行可扩展精确聚类
Parallel and Scalable Precise Clustering for Homologous Protein Discovery
摘要：
本文提出了一种新的，并行实现聚类，并证明了其效用，大大加快了同源蛋白质的鉴定过程。聚类是一种减少在一组$n$元素（如蛋白质序列）中找到相似对所需的比较次数的技术。精确的聚类可以确保每对相似的元素至少出现在一个聚类中，这样就可以通过每个聚类中的所有对所有的比较来识别相似性，而不是在整个集合中。本文介绍了一种新的算法，精确的聚类，使用元素之间的传递关系，使并行和可扩展的实现这种方法。我们将ChalleterMerge应用于在蛋白质集合中找到相似氨基酸序列的重要问题。通过完整的$O(n^2)$比较，QuarterMerge可以识别99.8%的相似对，而操作数只有前者的一半。更重要的是，QuarterMerge非常适合并行和分布式计算。我们的实现在768个核心上实现了604 $\times$的加速比（比可比的单线程集群实现快1400 $\times$），90%的强伸缩效率和近100%的弱伸缩效率。
英文摘要：
This paper presents a new, parallel implementation of clustering and demonstrates its utility in greatly speeding up the process of identifying homologous proteins. Clustering is a technique to reduce the number of comparison needed to find similar pairs in a set of $n$ elements such as protein sequences. Precise clustering ensures that each pair of similar elements appears together in at least one cluster, so that similarities can be identified by all-to-all comparison in each cluster rather than on the full set. This paper introduces ClusterMerge, a new algorithm for precise clustering that uses transitive relationships among the elements to enable parallel and scalable implementations of this approach. We apply ClusterMerge to the important problem of finding similar amino acid sequences in a collection of proteins. ClusterMerge identifies 99.8% of similar pairs found by a full $O(n^2)$ comparison, with only half as many operations. More importantly, ClusterMerge is highly amenable to parallel and distributed computation. Our implementation achieves a speedup of 604$\times$ on 768 cores (1400$\times$ faster than a comparable single-threaded clustering implementation), a strong scaling efficiency of 90%, and a weak scaling efficiency of nearly 100%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
