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Multi-parallel prefiltering on the convey HC-1 for supporting homology detection

Published: 15 September 2013 Publication History

Abstract

Gene databases used in research are huge and still grow at a fast pace. Many comparisons need to be done when searching similar (homologous) sequences in these databases for a given query sequence. Therefore, highly parallel architectures and much bandwidth are required for handling processing and transferring massive amounts of data. The Convey HC-1 with four FPGAs and high memory bandwidth of up to 76.8 GB/s seems very suitable for supporting this task as other bioinformatics applications have already been greatly supported by the HC-1. We research accelerating an application for searching homologous sequences. Limited by FPGA size only, we present a design that calculates 3 prefiltering scores per FPGA concurrently, i.e. 12 calculations in total. This score calculation for database sequences against the query profile is done by a modified Smith-Waterman scheme that is internally parallelized 16*8=128 times in contrast to the SSE implementation where only 16-fold parallelism can be exploited and where memory bandwidth poses the limiting factor. Preloading the query profile, we are able to transform the memory-bound SSE implementation to a compute-bound FPGA design which is only limited by FPGA size. Despite much lower clock rates, the FPGAs outperform SSE for the calculation of the prefiltering scores by a factor of 4.46. We achieve application speedup of 1.79 against the original, unmodified state-of-the-art SSE-based implementation because the score calculation accounts for less than 63% of the application runtime.

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Cited By

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  • (2024)An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov modelsBMC Bioinformatics10.1186/s12859-024-05879-325:1Online publication date: 29-Jul-2024
  • (2022)Efficient Homomorphic Convolution Designs on FPGA for Secure InferenceIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2022.319789530:11(1691-1704)Online publication date: Nov-2022
  • (2018)Combined hardware-software multi-parallel prefiltering on the Convey HC-1 for fast homology detectionParallel Computing10.1016/j.parco.2014.09.00642:C(4-17)Online publication date: 31-Dec-2018

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cover image ACM Other conferences
EuroMPI '13: Proceedings of the 20th European MPI Users' Group Meeting
September 2013
289 pages
ISBN:9781450319034
DOI:10.1145/2488551
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • ARCOS: Computer Architecture and Technology Area, Universidad Carlos III de Madrid

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New York, NY, United States

Publication History

Published: 15 September 2013

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EuroMPI '13
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  • ARCOS
EuroMPI '13: 20th European MPI Users's Group Meeting
September 15 - 18, 2013
Madrid, Spain

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EuroMPI '13 Paper Acceptance Rate 22 of 47 submissions, 47%;
Overall Acceptance Rate 66 of 139 submissions, 47%

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Cited By

View all
  • (2024)An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov modelsBMC Bioinformatics10.1186/s12859-024-05879-325:1Online publication date: 29-Jul-2024
  • (2022)Efficient Homomorphic Convolution Designs on FPGA for Secure InferenceIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2022.319789530:11(1691-1704)Online publication date: Nov-2022
  • (2018)Combined hardware-software multi-parallel prefiltering on the Convey HC-1 for fast homology detectionParallel Computing10.1016/j.parco.2014.09.00642:C(4-17)Online publication date: 31-Dec-2018

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