GPU-Accelerated GATK HaplotypeCaller with Load-Balanced Multi-Process Optimization

Shanshan Ren, Koen Bertels, Zaid Al-Ars

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)

Abstract

Due to its high-throughput and low cost, Next Generation Sequencing (NGS) technology is becoming increasingly popular in many genomics research labs. However, handling the massive raw data generated by the NGS platforms poses a significant computational challenge to genomics analysis tools. This paper presents a GPU acceleration of the GATK HaplotypeCaller (GATK HC), a widely used DNA variant caller in the clinic. Moreover, this paper proposes a load-balanced multi-process optimization of GATK HaplotypeCaller to address its implementation limitation which forces the sequential execution of the program and prevents effective utilization of hardware acceleration. In single-threaded mode, the GPU-based GATK HC is 1.71x and 1.21x faster than the baseline HC implementation and the vectorized GATK HC implementation, respectively. Moreover, the GPU-based implementation achieves up to 2.04x and 1.40x speedup in load-balanced multi-process mode over the baseline implementation and the vectorized GATK HC implementation in non-load-balanced multi-process mode, respectively.
Original languageEnglish
Title of host publication2017 IEEE 17th International Conference on BioInformatics and BioEngineering (BIBE)
Place of PublicationPiscataway
PublisherIEEE
Pages497-502
Number of pages6
ISBN (Electronic)978-1-5386-1324-5
ISBN (Print)978-1-5386-1325-2
DOIs
Publication statusPublished - 2017
EventBIBE 2017: 17th IEEE International Conference on BioInformatics and BioEngineering - Washington DC, United States
Duration: 23 Oct 201725 Oct 2017
http://bibe2017.com/index.html

Conference

ConferenceBIBE 2017
Abbreviated titleBIBE 2017
Country/TerritoryUnited States
CityWashington DC
Period23/10/1725/10/17
Internet address

Keywords

  • Bioinformatics
  • Genomics
  • Graphics processing units
  • Acceleration
  • DNA
  • Optimization
  • Sequential analysis

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