An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models
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| Izdano u: | BMC Bioinformatics vol. 25 (2024), p. 1 |
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Springer Nature B.V.
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| Online pristup: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3091290016 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1471-2105 | ||
| 024 | 7 | |a 10.1186/s12859-024-05879-3 |2 doi | |
| 035 | |a 3091290016 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 58459 |2 nlm | ||
| 100 | 1 | |a Anderson, Tim | |
| 245 | 1 | |a An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models | |
| 260 | |b Springer Nature B.V. |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundSequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER.ResultsThe HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a \(\sim\) $3000 Xilinx Alveo U50 FPGA accelerator card, \(\sim\) 227× faster than the optimized SSV implementation in nhmmer. Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer’s SSV with one thread and 35× faster than nhmmer with four threads, and uses \(\sim\) 31% the energy of a traditional high end Intel CPU.ConclusionsHAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline. | |
| 653 | |a Data transfer (computers) | ||
| 653 | |a Central processing units--CPUs | ||
| 653 | |a Alignment | ||
| 653 | |a Data processing | ||
| 653 | |a Markov chains | ||
| 653 | |a Accelerator cards | ||
| 653 | |a Hardware | ||
| 653 | |a Homology | ||
| 653 | |a Microprocessors | ||
| 653 | |a Bioinformatics | ||
| 653 | |a Pipelining (computers) | ||
| 653 | |a Genomes | ||
| 653 | |a Annotations | ||
| 653 | |a Algorithms | ||
| 653 | |a Field programmable gate arrays | ||
| 653 | |a Nucleotide sequence | ||
| 653 | |a Bus interconnections | ||
| 700 | 1 | |a Wheeler, Travis J | |
| 773 | 0 | |t BMC Bioinformatics |g vol. 25 (2024), p. 1 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3091290016/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3091290016/fulltext/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3091290016/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |