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Sequence alignment is a crucial step in next-generation sequencing data analysis. However, most sequence aligners face performance challenges due to high computational complexity and extensive random memory access patterns, making them a significant bottleneck in the overall analysis pipeline, such as the industry gold standard BWA-MEM. The next-generation Sunway platform, with its high computational power and unique heterogeneous architecture, presents new opportunities for enhancing the efficiency of sequence alignment. In this work, we introduce SWBWA, a high-accuracy and high-performance sequence aligner designed for the new Sunway architecture. By redesigning the parallel framework tailored for Sunway, performing software prefetching optimization, vectorizing the striped Smith-Waterman algorithm, and addressing memory access bottlenecks in bigshare mode, SWBWA achieves a 330 \(\times \) speedup over the single-threaded unoptimized version. Additionally, SWBWA running on a Sunway workstation can achieve 1.2–1.4 \(\times \) speedups compared to BWA-MEM running on a dual-socket 48-core x86 server, while ensuring nearly identical output. The source code is publicly available at https://github.com/RabbitBio/SWBWA .

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SWBWA: A Highly Efficient NGS Aligner on the New Sunway Architecture

  • 会议论文

  • 发表日期：

    2026年

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  • Lifeng Yan,
  • Zekun Yin,
  • Qixin Chang,
  • Tong Zhang,
  • Zhisong Wang,
  • Xiaohui Duan,
  • Bertil Schmidt,
  • Weiguo Liu

摘要

Sequence alignment is a crucial step in next-generation sequencing data analysis. However, most sequence aligners face performance challenges due to high computational complexity and extensive random memory access patterns, making them a significant bottleneck in the overall analysis pipeline, such as the industry gold standard BWA-MEM. The next-generation Sunway platform, with its high computational power and unique heterogeneous architecture, presents new opportunities for enhancing the efficiency of sequence alignment. In this work, we introduce SWBWA, a high-accuracy and high-performance sequence aligner designed for the new Sunway architecture. By redesigning the parallel framework tailored for Sunway, performing software prefetching optimization, vectorizing the striped Smith-Waterman algorithm, and addressing memory access bottlenecks in bigshare mode, SWBWA achieves a 330 \(\times \) speedup over the single-threaded unoptimized version. Additionally, SWBWA running on a Sunway workstation can achieve 1.2–1.4 \(\times \) speedups compared to BWA-MEM running on a dual-socket 48-core x86 server, while ensuring nearly identical output. The source code is publicly available at https://github.com/RabbitBio/SWBWA .