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
Number of pages6
ISBN (Electronic)978-1-5386-1324-5
ISBN (Print)978-1-5386-1325-2
Publication statusPublished - 2017
EventBIBE 2017: 17th IEEE International Conference on BioInformatics and BioEngineering - Washington DC, United States
Duration: 23 Oct 201725 Oct 2017


ConferenceBIBE 2017
Abbreviated titleBIBE 2017
CountryUnited States
CityWashington DC
Internet address

    Research areas

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

ID: 29695543