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<p>With the rapid development of Next-Generation Sequencing (NGS) technology, genome sequencing services for clinical fields are now bringing new challenges to existing solutions. The increasing demand for alignment data processing motivates the development of more efficient algorithms for computational genomics. The Pair-Hidden Markov Model (Pair-HMM) is one of the most popular models used to process sequence alignment. Its related Forward Algorithm (FA) is usually the key performance bottleneck of the entire variant calling workflow. While multiple previous works have been conducted in efforts to accelerate the algorithm with various levels of parallelization, it still lacks of fully utilizing the resources of heterogeneous devices, such as high-bandwidth memory and massive SIMD cores in advanced GPU. In this paper, we design a GPU-based Pari-HMM sequence alignment algorithm and conduct its implementation with holistic co-design optimizations, including efficient computational parallelization, parameter initialization, memory accessing layout, and etc. When using Nvidia Telsa V100 GPU, Our work has shown speedups of 1151x compared to the Java baseline on Intel single-core CPU and 1.47x to the previous state-of-art GPU work.</p>

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GPU acceleration for DNA sequence alignment algorithm and its application

  • 期刊论文

  • 发表日期：

    2025年2月15日

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  • Heming Zhong,
  • Xiaojian Pan,
  • Zengquang He,
  • Haoling Wang,
  • Dan Huang,
  • Zhiguang Chen

摘要

With the rapid development of Next-Generation Sequencing (NGS) technology, genome sequencing services for clinical fields are now bringing new challenges to existing solutions. The increasing demand for alignment data processing motivates the development of more efficient algorithms for computational genomics. The Pair-Hidden Markov Model (Pair-HMM) is one of the most popular models used to process sequence alignment. Its related Forward Algorithm (FA) is usually the key performance bottleneck of the entire variant calling workflow. While multiple previous works have been conducted in efforts to accelerate the algorithm with various levels of parallelization, it still lacks of fully utilizing the resources of heterogeneous devices, such as high-bandwidth memory and massive SIMD cores in advanced GPU. In this paper, we design a GPU-based Pari-HMM sequence alignment algorithm and conduct its implementation with holistic co-design optimizations, including efficient computational parallelization, parameter initialization, memory accessing layout, and etc. When using Nvidia Telsa V100 GPU, Our work has shown speedups of 1151x compared to the Java baseline on Intel single-core CPU and 1.47x to the previous state-of-art GPU work.