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在GPU上加速BWA-MEM读取映射.

Minh Pham1, Yicheng Tu1, Xiaoyi Lv2

  • 1University of South Florida, Tampa, FL, USA.

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PubMed
概括
此摘要是机器生成的。

这项研究在GPU上实施了BWA-MEM短读映射程序,实现了显著的加速度. 与基于CPU的方法相比,GPU实现提供了更快的DNA序列分析.

关键词:
这就是BWA BWA BWA.我们的GPU是GPU的GPU大规模并行算法的大规模并行算法国家货币体系对齐情况

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 下一代测序 (NGS) 生成大量的DNA数据,需要高效的分析工具.
  • 将短DNA读取到参考基因组的映射是NGS数据分析中的计算密集的瓶.
  • BWA-MEM和BWA-MEM2是阅读映射的流行的CPU优化算法.

研究的目的:

  • 在图形处理单元 (GPU) 上实现BWA-MEM读取映射算法.
  • 识别和解决适应BWA-MEM的算法和数据结构用于GPU架构的挑战.
  • 为了评估GPU实现的性能与已建立的基于CPU的版本相比.

主要方法:

  • 开发了BWA-MEM算法的GPU实现,解决了播种,种子链,史密斯-沃特曼对齐,内存管理和I/O.
  • 使用NVIDIA A40,A6000和A100 GPU与运行BWA-MEM和BWA-MEM2的64线程CPU进行了性能比较.
  • 进行阶段分析,以评估关键计算阶段的加速.

主要成果:

  • 在高端GPU上,比BWA-MEM提高6.8倍,比BWA-MEM2提高3.8倍.
  • 在播种/链接 (高达4倍),史密斯-沃特曼 (高达2.5倍) 和SAM输出生成 (高达7.9倍) 中展示了显著的加速度.
  • 这代表了第一个已知的尝试在GPU上实现整个BWA-MEM程序.

结论:

  • BWA-MEM的GPU加速是可行的,并为DNA序列映射提供了实质性的性能改进.
  • 开发的GPU实现解决了关键的计算挑战,为NGS数据分析提供了更快的替代方案.
  • 这项工作为更高效的基因组数据处理管道铺平了道路.