Genome sequencing using mapreduce on FPGA with multiple hardware accelerators (abstract only)

  • Authors:

  • Chao Wang;Xi Li;Xuehai Zhou;Jim Martin;Ray C.C. Cheung

  • Affiliations:

  • University of Science and Technology of China, Suzhou, China;University of Science and Technology of China, Hefei, China;University of Science and Technology of China, Hefei, China;Clemson University, Clemson, SC, USA;City University of Hong Kong, Hong Kong, Hong Kong

  • Venue:
  • Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
  • Year:
  • 2013

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Abstract

The genome sequencing problem with short reads is an emerging field with seemingly limitless possibilities for advances in numerous scientific research and application domains. It has been the hot topic during the past few years. Growing with the data population and the ease to access for personal users, how to shorten the response interval for short read mapping at a large scale computing domain is extremely important. In this paper we propose a novel FPGA-based acceleration solution with Map-Reduce framework on multiple hardware acceleration engines. The combination of hardware accelerators and Map-Reduce execution flow could greatly expedite the task of aligning short length reads to a known reference genome. Our approach is based on preprocessing the reference genomes and iterative jobs for aligning the continuous incoming reads. The read-mapping algorithm is modeled after the creditable RMAP software approach. Furthermore, theoretical speedup analysis on a MapReduce programming platform is presented, which demonstrates that our proposed architecture has efficient potential to reduce the average waiting time for large scale short reads applications.