下一代测序(NGS)的进步显着降低了生成DNA序列数据的成本,并提高了数据生成的速度。然而,这种高通量数据生产增加了对高效数据分析程序的需求。分析测序数据中计算最苛刻的步骤之一是将NGS产生的短读数映射到参考DNA序列。比如人类基因组。映射程序BWA-MEM及其较新版本BWA-MEM2,针对CPU进行了优化,是这项任务的一些最受欢迎的选择。在这项研究中,我们讨论了BWA-MEM在GPU上的实现。这是一项具有挑战性的任务,因为BWA-MEM中的许多算法和数据结构在GPU架构上无法有效执行。本文指出了在BWA-MEM程序的所有主要阶段开发高效GPU代码的主要挑战,包括播种,种子链,史密斯-沃特曼对齐,内存管理,和I/O处理。我们对在64线程CPU上运行的BWA-MEM和BWA-MEM2进行了比较实验。结果表明,使用NVIDIAA40时,我们的实施比BWA-MEM2实现了高达3.2倍的加速,比BWA-MEM实现了高达5.8倍的加速。使用NVIDIAA6000和NVIDIAA100,我们实现了比BWA-MEM2高3.4x/3.8x的靠墙时间加速,比BWA-MEM高6.1x/6.8x的靠墙时间加速。分别。在阶段比较中,A40/A6000/A100GPU分别达到3.7/3.8/4x,2/2.3/2.5x,和3.1/5/7.9x加速BWA-MEM的三个主要阶段:播种和种子链,史密斯-沃特曼,并使SAM输出。据我们所知,这是第一个尝试在GPU上实现整个BWA-MEM程序的研究。
Advancements in Next-Generation Sequencing (NGS) have significantly reduced the cost of generating DNA sequence data and increased the speed of data production. However, such high-throughput data production has increased the need for efficient data analysis programs. One of the most computationally demanding steps in analyzing sequencing data is mapping short reads produced by NGS to a reference DNA sequence, such as a human genome. The mapping program
BWA-MEM and its newer version
BWA-MEM2, optimized for CPUs, are some of the most popular choices for this task. In this study, we discuss the implementation of
BWA-MEM on GPUs. This is a challenging task because many algorithms and data structures in BWA-MEM do not execute efficiently on the GPU architecture. This paper identifies major challenges in developing efficient GPU code on all major stages of the BWA-MEM program, including seeding, seed chaining, Smith-Waterman alignment, memory management, and I/O handling. We conduct comparison experiments against
BWA-MEM and
BWA-MEM2 running on a 64-thread CPU. The results show that our implementation achieved up to 3.2x speedup over BWA-MEM2 and up to 5.8x over
BWA-MEM when using an NVIDIA A40. Using an NVIDIA A6000 and an NVIDIA A100, we achieved a wall-time speedup of up to 3.4x/3.8x over BWA-MEM2 and up to 6.1x/6.8x over
BWA-MEM, respectively. In stage-wise comparison, the A40/A6000/A100 GPUs respectively achieved up to 3.7/3.8/4x, 2/2.3/2.5x, and 3.1/5/7.9x speedup on the three major stages of BWA-MEM: seeding and seed chaining, Smith-Waterman, and making SAM output. To the best of our knowledge, this is the first study that attempts to implement the entire BWA-MEM program on GPUs.