A HMMER hardware accelerator using divergences

Abstract

As new protein sequences are discovered on an everyday basis and protein databases continue to grow exponentially with time, computational tools take more and more time to search protein databases to discover the common ancestors of them. HMMER is among the most used tools in protein search and comparison and multiple efforts have been made to accelerate its execution by using dedicated hardware prototyped on FPGAs. In this paper we introduce a novel algorithm called the Divergence Algorithm, which not only enables the FPGA accelerator to reduce execution time, but also enables further acceleration of the alignment generation algorithm of the HMMER programs by reducing the number of cells of the Dynamic Programming matrices it has to calculate. We also propose a more accurate performance measurement strategy that considers all the execution times while doing protein searches and alignments, while other works only consider hardware execution times and do not include alignment generation times. Using our proposed hardware accelerator and the Divergence Algorithm, we were able to achieve gains up to 182x when compared to the unaccelerated HMMER software running on a general purpose CPU.

Accelerating Phylogeny-Aware Short DNA Read Alignment with FPGAs

“…There also exists related work on accelerating the (more complex) dynamic programming kernel of HM-MER [5]. For an overview of FPGA accelerator architectures for the Virterbi algorithm used in HMMER, please refer to [18]. Performance results vary between 0.7 and 20 million CUPS.…”

Combining execution pipelines to improve parallel implementation of HMMER on FPGA

“…Very recently, Giraldo et al [20] proposed another approach. They simplified (without J state) Viterbi kernel as a filter and passes only sequences with significant scores to original Viterbi kernel along with divergence algorithm [21] data.…”