Abstract
Pattern matching is a versatile task which has a variety of applications including genome sequencing as a major application. During the analysis, short read mapping technique is used where short DNA sequences are mapped relative to a known reference sequence. This paper discusses the use of reconfigurable hardware to accelerate the short read mapping problem. The proposed design is based on the FM-index algorithm. Although several pattern matching techniques are available, FM-index based pattern search is perfectly suitable for genome sequencing due to the fastest mapping from known indices. In order to make use of inherent parallelism, a multi-FPGA system called Flow-in-Cloud (FiC) is used. FiC consists of multiple boards, mounting middle scale Xilinx’s FPGAs and SDRAMs, which are tightly coupled with high speed serial links. By distributing the input data transfer with I/O ring network and broadcasting I-Table, C-Table and Suffix-Array with the board-to-board interconnection network, about 10 times performance improvement was achieved when compared to the software implementation. Since the proposed method is scalable to the number of boards, we can obtain the required performance by increasing the number of boards.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arram, J., Kaplan, T., Luk, W., Jiang, P.: Leveraging FPGAs for accelerating short read alignment. IEEE/ACM Trans. Comput. Biol. Bioinform. 14, 668–677 (2017)
Arram, J., Tsoi, K.H., Luk, W., Jiang, P.: Hardware acceleration of genetic sequence alignment. In: Brisk, P., de Figueiredo Coutinho, J.G., Diniz, P.C. (eds.) ARC 2013. LNCS, vol. 7806, pp. 13–24. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36812-7_2
Burrows, M., Wheeler, D.J.: A block-sorting lossless data compression algorithm. Technical report (1994)
Fernandez, E., Najjar, W., Lonardi, S.: String matching in hardware using the FM-index. In: 2011 IEEE 19th Annual International Symposium on Field-Programmable Custom Computing Machines, pp. 218–225, May 2011
Ferragina, P., Manzini, G.: Opportunistic data structures with applications. In: Proceedings 41st Annual Symposium on Foundations of Computer Science, pp. 390–398, November 2000
Hironaka, K., Doan, N.A.V., Amano, H.: Towards an optimized multi FPGA architecture with STDM network: a preliminary study. In: Voros, N., Huebner, M., Keramidas, G., Goehringer, D., Antonopoulos, C., Diniz, P.C. (eds.) ARC 2018. LNCS, vol. 10824, pp. 142–150. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78890-6_12
Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L.: Ultrafast and memory-efficient alignment of short dna sequences to the human genome. Genome Biol. 10(3), R25 (2009)
Li, H., Durbin, R.: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14), 1754–1760 (2009)
Liu, C.-M., et al.: SOAP3: ultra-fast GPU-based parallel alignment tool for short reads. Bioinformatics 28(6), 878–879 (2012)
Olson, C., et al.: Hardware acceleration of short read mapping. In: IEEE 20th International Symposium on Field-Programmable Custom Computing Machines (FCCM), pp. 161–168 (2012)
Samtec, Inc.: Micro Flyover On-Board Optical Engine, FireFly. https://www.samtec.com/optics/optical-cable/mid-board/firefly
Yamakura, M., Hironaka, K., Azegami, K., Musha, K., Amano, H.: The evaluation of partial reconfiguration for a multi-board FPGA system FiCsw. In: Proceedings of the 10th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, HEART 2019, pp. 15:1–15:4 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ullah, M.M.I., Ben Ahmed, A., Amano, H. (2020). Implementation of FM-Index Based Pattern Search on a Multi-FPGA System. In: Rincón, F., Barba, J., So, H., Diniz, P., Caba, J. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2020. Lecture Notes in Computer Science(), vol 12083. Springer, Cham. https://doi.org/10.1007/978-3-030-44534-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-44534-8_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44533-1
Online ISBN: 978-3-030-44534-8
eBook Packages: Computer ScienceComputer Science (R0)