Evaluating Optimization Strategies for HMMer Acceleration on GPU

Abstract

Comparing a biological sequence to a family of sequences is an important task in Bioinformatics, commonly performed using tools such as HMMer. The Viterbi algorithm is applied as HMMer main step to compute the similarity between the sequence and the family. Due to the exponential growth of biological sequence databases, implementations of the Viterbi algorithm on several high performance platforms have been proposed. Nevertheless, few implementations of the Viterbi algorithm use GPUs as main platform. In this paper, we present the development and optimization of an accelerator for the Viterbi algorithm applied to biological sequence analysis on GPUs. Some of the optimizations analyzed are applied to the sequence comparison problem for the first time in the literature and others are evaluated in more depth than in related works. Our main contributions are: (a) an accelerator that achieves speedups up to 102.90 and 60.46, with respect to HMMer2 and HMMer3 execution on a general purpose computer, respectively, (b) the use of the multi-platform OpenCL programming model for the accelerator, (c) a detailed evaluation of several optimizations such as memory, control flow, execution space, instruction scheduling, and loop optimizations, and (d) a methodology of optimizations and evaluation that can also be applied to other sequence comparison algorithms, such as the HMMer3 MSV.