Artificial intelligence and molecular biology
Artificial intelligence and molecular biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
ClawHMMER: A Streaming HMMer-Search Implementatio
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Accelerator design for protein sequence HMM search
Proceedings of the 20th annual international conference on Supercomputing
MPI-HMMER-Boost: Distributed FPGA Acceleration
Journal of VLSI Signal Processing Systems
A parallel strategy for biological sequence alignment in restricted memory space
Journal of Parallel and Distributed Computing
A High Performance Reconfigurable Core for Motif Searching Using Profile HMM
AHS '08 Proceedings of the 2008 NASA/ESA Conference on Adaptive Hardware and Systems
HMMer acceleration using systolic array based reconfigurable architecture
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
High speed biological sequence analysis with hiddenMarkov models on reconfigurable platforms
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Multi-parallel prefiltering on the convey HC-1 for supporting homology detection
Proceedings of the 20th European MPI Users' Group Meeting
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The Viterbi algorithm is one of the most used dynamic programming algorithms for protein comparison and identification, based on hidden markov Models (HMMs). Most of the works in the literature focus on the implementation of hardware accelerators that act as a prefilter stage in the comparison process. This stage discards poorly aligned sequences with a low similarity score and forwards sequences with good similarity scores to software, where they are reprocessed to generate the sequence alignment. In order to reduce the software reprocessing time, this work proposes a hardware accelerator for the Viterbi algorithm which includes the concept of divergence, in which the region of interest of the dynamic programming matrices is delimited. We obtained gains of up to 182x when compared to unaccelerated software. The performance measurement methodology adopted in this work takes into account not only the acceleration achieved by the hardware but also the reprocessing software stage required to generate the alignment.