Accelerating HMMer on FPGAs using systolic array based architecture

HMMer is a widely-used bioinformatics software package that uses profile HMMs (Hidden Markov Models) to model the primary structure consensus of a family of protein or nucleic acid sequences. However, with the rapid growth of both sequence and model databases, it is more and more time-consuming to run HMMer on traditional computer architecture. In this paper, the computation kernel of HMMer, P7Viterbi, is selected to be accelerated by FPGA. There is an infrequent feedback loop in P7Viterbi to update the value of beginning state (B state), which limits further parallelization. Previous work either ignored the feedback loop or serialized the process, leading to loss of either precision or efficiency. Our proposed syslolic array based architecture with a parallel data providing unit can exploit maximum parallelism of the full version of P7Viterbi. The proposed architecture speculatively runs with fully parallelism assuming that the feedback loop does not take place. If the rare feedback case actually occurs, a rollback mechanism is used to ensure correctness. Results show that by using Xilinx Virtex-5 110T FPGA, the proposed architecture with 20 PEs can achieve about a 56.8 times speedup compared with that of Intel Core2 Duo 2.33 GHz CPU.