IEICE TRANSACTIONS on Information
A Multi-FPGA Implementation of FM-Index Based Genomic Pattern Search
Summary :
FPGA clusters that consist of multiple FPGA boards have been gaining interest in recent times. Massively parallel processing with a stand-alone heterogeneous FPGA cluster with SoC- style FPGAs and mid-scale FPGAs is promising with cost-performance benefit. Here, we propose such a heterogeneous FPGA cluster with FiC and M-KUBOS cluster. FiC consists of multiple boards, mounting middle scale Xilinx's FPGAs and DRAMs, which are tightly coupled with high-speed serial links. In addition, M-KUBOS boards are connected to FiC for ensuring high IO data transfer bandwidth. As an example of massively parallel processing, here we implement genomic pattern search. Next-generation sequencing (NGS) technology has revolutionized biological system related research by its high-speed, scalable and massive throughput. To analyze the genomic data, short read mapping technique is used where short Deoxyribonucleic acid (DNA) sequences are mapped relative to a known reference sequence. Although several pattern matching techniques are available, FM-index based pattern search is perfectly suitable for this task due to the fastest mapping from known indices. Since matching can be done in parallel for different data, the massively parallel computing which distributes data, executes in parallel and gathers the results can be applied. We also implement a data compression method where about 10 times reduction in data size is achieved. We found that a M-KUBOS board matches four FiC boards, and a system with six M-KUBOS boards and 24 FiC boards achieved 30 times faster than the software based implementation.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.11 pp.1783-1795
- Publication Date
- 2023/11/01
- Publicized
- 2023/08/09
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDP7230
- Type of Manuscript
- PAPER
- Category
- Computer System
Authors
Ullah IMDAD
Keio University
Akram BEN AHMED
National Institute of Advanced Industrial Science and Technology (AIST)
Kazuei HIRONAKA
Keio University
Kensuke IIZUKA
Keio University
Hideharu AMANO
Keio University
Keyword
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Ullah IMDAD, Akram BEN AHMED, Kazuei HIRONAKA, Kensuke IIZUKA, Hideharu AMANO, "A Multi-FPGA Implementation of FM-Index Based Genomic Pattern Search" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 11, pp. 1783-1795, November 2023, doi: 10.1587/transinf.2022EDP7230.
Abstract: FPGA clusters that consist of multiple FPGA boards have been gaining interest in recent times. Massively parallel processing with a stand-alone heterogeneous FPGA cluster with SoC- style FPGAs and mid-scale FPGAs is promising with cost-performance benefit. Here, we propose such a heterogeneous FPGA cluster with FiC and M-KUBOS cluster. FiC consists of multiple boards, mounting middle scale Xilinx's FPGAs and DRAMs, which are tightly coupled with high-speed serial links. In addition, M-KUBOS boards are connected to FiC for ensuring high IO data transfer bandwidth. As an example of massively parallel processing, here we implement genomic pattern search. Next-generation sequencing (NGS) technology has revolutionized biological system related research by its high-speed, scalable and massive throughput. To analyze the genomic data, short read mapping technique is used where short Deoxyribonucleic acid (DNA) sequences are mapped relative to a known reference sequence. Although several pattern matching techniques are available, FM-index based pattern search is perfectly suitable for this task due to the fastest mapping from known indices. Since matching can be done in parallel for different data, the massively parallel computing which distributes data, executes in parallel and gathers the results can be applied. We also implement a data compression method where about 10 times reduction in data size is achieved. We found that a M-KUBOS board matches four FiC boards, and a system with six M-KUBOS boards and 24 FiC boards achieved 30 times faster than the software based implementation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7230/_p
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@ARTICLE{e106-d_11_1783,
author={Ullah IMDAD, Akram BEN AHMED, Kazuei HIRONAKA, Kensuke IIZUKA, Hideharu AMANO, },
journal={IEICE TRANSACTIONS on Information},
title={A Multi-FPGA Implementation of FM-Index Based Genomic Pattern Search},
year={2023},
volume={E106-D},
number={11},
pages={1783-1795},
abstract={FPGA clusters that consist of multiple FPGA boards have been gaining interest in recent times. Massively parallel processing with a stand-alone heterogeneous FPGA cluster with SoC- style FPGAs and mid-scale FPGAs is promising with cost-performance benefit. Here, we propose such a heterogeneous FPGA cluster with FiC and M-KUBOS cluster. FiC consists of multiple boards, mounting middle scale Xilinx's FPGAs and DRAMs, which are tightly coupled with high-speed serial links. In addition, M-KUBOS boards are connected to FiC for ensuring high IO data transfer bandwidth. As an example of massively parallel processing, here we implement genomic pattern search. Next-generation sequencing (NGS) technology has revolutionized biological system related research by its high-speed, scalable and massive throughput. To analyze the genomic data, short read mapping technique is used where short Deoxyribonucleic acid (DNA) sequences are mapped relative to a known reference sequence. Although several pattern matching techniques are available, FM-index based pattern search is perfectly suitable for this task due to the fastest mapping from known indices. Since matching can be done in parallel for different data, the massively parallel computing which distributes data, executes in parallel and gathers the results can be applied. We also implement a data compression method where about 10 times reduction in data size is achieved. We found that a M-KUBOS board matches four FiC boards, and a system with six M-KUBOS boards and 24 FiC boards achieved 30 times faster than the software based implementation.},
keywords={},
doi={10.1587/transinf.2022EDP7230},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - A Multi-FPGA Implementation of FM-Index Based Genomic Pattern Search
T2 - IEICE TRANSACTIONS on Information
SP - 1783
EP - 1795
AU - Ullah IMDAD
AU - Akram BEN AHMED
AU - Kazuei HIRONAKA
AU - Kensuke IIZUKA
AU - Hideharu AMANO
PY - 2023
DO - 10.1587/transinf.2022EDP7230
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2023
AB - FPGA clusters that consist of multiple FPGA boards have been gaining interest in recent times. Massively parallel processing with a stand-alone heterogeneous FPGA cluster with SoC- style FPGAs and mid-scale FPGAs is promising with cost-performance benefit. Here, we propose such a heterogeneous FPGA cluster with FiC and M-KUBOS cluster. FiC consists of multiple boards, mounting middle scale Xilinx's FPGAs and DRAMs, which are tightly coupled with high-speed serial links. In addition, M-KUBOS boards are connected to FiC for ensuring high IO data transfer bandwidth. As an example of massively parallel processing, here we implement genomic pattern search. Next-generation sequencing (NGS) technology has revolutionized biological system related research by its high-speed, scalable and massive throughput. To analyze the genomic data, short read mapping technique is used where short Deoxyribonucleic acid (DNA) sequences are mapped relative to a known reference sequence. Although several pattern matching techniques are available, FM-index based pattern search is perfectly suitable for this task due to the fastest mapping from known indices. Since matching can be done in parallel for different data, the massively parallel computing which distributes data, executes in parallel and gathers the results can be applied. We also implement a data compression method where about 10 times reduction in data size is achieved. We found that a M-KUBOS board matches four FiC boards, and a system with six M-KUBOS boards and 24 FiC boards achieved 30 times faster than the software based implementation.
ER -
English
