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Hardware Acceleration of HMMER on FPGAs

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Abstract

We propose a new parallelization scheme for the hmmsearch function of the HMMER software, in order to target FPGA technology. hmmsearch is a very compute intensive software for biological sequence alignment, based on profile hidden Markov models. We derive a flexible, generic, scalable hardware parallel architecture which can accelerate the core of hmmsearch by nearly two orders of magnitude, without modifying the original algorithm of this software. Our derivation is based on the expression of the algorithm as a set of recurrence equations, and we show in a systematic way how a very efficient parallel version of the algorithm can be found by combining scheduling, projection, partitioning, pipelining and precision analysis. We present the performance of the implementation of this parallel algorithm on a FPGA platform.

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Authors and Affiliations

  1. IRISA, Rennes, France

    Steven Derrien & Patrice Quinton

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  1. Steven Derrien
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  2. Patrice Quinton
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Corresponding author

Correspondence to Steven Derrien.

Appendix

Appendix

Table 6 System of Uniform Recurrent Equations (SURE) for the P7Viterbi  loop nest. M max is the maximum HMM model size, M is the size of the HMM model at hand, and L is the sequence length. \(\mathcal{D}\) is the domain \(\left\{(i,k)\: | \: 1 \!\leq\! i \!\leq\! L,\: 1 \!\leq\! k \!\leq\! M \right\}\).
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Table 7 Original source code for P7Viterbi routine.
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Derrien, S., Quinton, P. Hardware Acceleration of HMMER on FPGAs. J Sign Process Syst Sign Image Video Technol 58, 53–67 (2010). https://doi.org/10.1007/s11265-008-0262-y

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