在多核架构上扩展hmm性能

S. Isaza, Ernst Houtgast, Friman Sánchez, Alex Ramírez, G. Gaydadjiev
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引用次数: 3

摘要

在生物信息学中,蛋白质序列比对是科学家的基本任务之一。由于生物数据呈指数级增长,因此对计算能力的需求不断增加。随着当前处理器技术向多核的使用方向发展,应用程序的映射和并行化已经成为一个关键问题。为了跟上处理需求,需要找到应用程序的性能瓶颈,并适当地加以解决。本文研究了生物信息学应用HMMER进行序列比对的并行性和性能可扩展性。在研究了移植到Cell处理器的hmm版本中的瓶颈之后,我们提出了两个优化版本,以提高更大的多核架构中的可伸缩性。我们使用一个模拟器来模拟一个多达512个处理器的系统,并研究三个并行版本的HMMER的性能。结果表明,对于短HMM查询和长HMM查询,消除I/O瓶颈分别使性能提高了3倍和2.4倍。此外,通过将序列预格式化卸载到工作内核,可以实现高达27倍和7倍的更快速度。与使用单个工作处理器相比,使用256核时可获得高达156X的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scaling HMMER Performance on Multicore Architectures
In bioinformatics, protein sequence alignment is one of the fundamental tasks that scientists perform. Since the growth of biological data is exponential, there is an ever-increasing demand for computational power. While current processor technology is shifting towards the use of multicores, the mapping and parallelization of applications has become a critical issue. In order to keep up with the processing demands, applications' bottlenecks to performance need to be found and properly addressed. In this paper we study the parallelism and performance scalability of HMMER, a bioinformatics application to perform sequence alignment. After our study of the bottlenecks in a HMMER version ported to the Cell processor, we present two optimized versions to improve scalability in a larger multicore architecture. We use a simulator that allows us to model a system with up to 512 processors and study the performance of the three parallel versions of HMMER. Results show that removing the I/O bottleneck improves performance by 3X and 2.4X for a short and a long HMM query respectively. Additionally, by offloading the sequence pre-formatting to the worker cores, larger speedups of up to 27X and 7X are achieved. Compared to using a single worker processor, up to 156X speedup is obtained when using 256 cores.
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