Algorithm-Hardware Co-design for BQSR Acceleration in Genome Analysis ToolKit

Michael Lo, Zhenman Fang, Jie Wang, Peipei Zhou, Mau Chung Frank Chang, Jason Cong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Genome sequencing is one of the key applications in healthcare and has a great potential to realize precision medicine and personalized healthcare. However, its computing process is very time consuming. Even pre-processing the raw sequence data of a whole genome for a single person to the analysis ready data can take several days on a single-core CPU.In this paper, we propose to accelerate the performance of the widely used Genome Analysis ToolKit (GATK) using FPGAs. More specifically, we focus on the algorithm and hardware co-design for the Base Quality Score Re-calibration (BQSR) step in GATK, which is an important and time-consuming step to correct systematic errors made by a sequencing machine. Prior studies did not consider hardware acceleration for BQSR because it requires a large amount of memory with random access and has a lot of control flow. To address these challenges, we first adapt the algorithm to resolve the random memory access conflicts to achieve a fully pipelined accelerator design and reduce its dataset size. Second, we leverage the newly introduced large-capacity UltraRAM (URAM) in Xilinx UltraScale+ FPGAs to butter BQSR's large dataset on chip, and further optimize its operating frequency. Finally, we also explore the coarse-grained pipeline and parallelism to improve the overall performance of the BQSR accelerator. Compared to the latest software implementation of BQSR on GATK 4.1, running on single-thread and 56-thread CPUs (14nm Xeon E5-2680 v4), our FPGA accelerator running on Xilinx 16nmUltraScale+VCUl525 board achieves up to 40. 7x and 8. 5x speedups, respectively.

Original languageEnglish
Title of host publicationProceedings - 28th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages157-166
Number of pages10
ISBN (Electronic)9781728158037
DOIs
StatePublished - May 2020
Event28th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020 - Fayetteville, United States
Duration: 3 May 20206 May 2020

Publication series

NameProceedings - 28th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020

Conference

Conference28th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020
CountryUnited States
CityFayetteville
Period3/05/206/05/20

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  • Cite this

    Lo, M., Fang, Z., Wang, J., Zhou, P., Chang, M. C. F., & Cong, J. (2020). Algorithm-Hardware Co-design for BQSR Acceleration in Genome Analysis ToolKit. In Proceedings - 28th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020 (pp. 157-166). [9114875] (Proceedings - 28th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FCCM48280.2020.00029