Scaling HMMER Performance on Multicore Architectures

• Main Authors: Isaza, S., Houtgast, E., Sanchez, F., Ramirez, A., Gaydadjiev, G.
• Format: Conference Proceeding
• Language: English
• Subjects: bioinformatics; Hidden Markov models; hmmer; Microprocessors; multicore architectures; Multicore processing; performance scalability; Scalability; Viterbi algorithm
• DOI: 10.1109/CISIS.2011.102

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.