LOGAN：基于GPU的高性能X-Drop长读比对
LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment
摘要：
成对序列比对是基因组数据分析中计算量最大的核心之一，占关键生物信息学应用运行时间的90%以上。由于分析长度在1 Kb和1 Mb之间的序列的高计算成本，这种方法对于第三代序列特别昂贵。鉴于精确配对算法的二次开销长alignment，社区主要依赖于近似算法，只搜索高质量的alignment，并在找不到时提前停止。在这项工作中，我们提出了流行的X-drop alignment算法的第一个GPU优化，我们命名为LOGAN。结果表明，我们的高性能多GPU实现高达181.6 GCUPS和速度高达6.6倍和30.7倍，分别使用1和6个NVIDIA Tesla V100，在最先进的软件上运行两个IBM Power 9处理器使用168 CPU线程，具有相同的精度。我们还证明了一个2.3倍的LOGAN速度比ksw 2，一个国家的最先进的矢量化算法的序列比对中实现minimap 2，一个长读映射软件。为了突出我们的工作对实际应用的影响，我们将LOGAN与一个名为BELLA的多对多长读段比对软件结合起来，并证明我们的实现将整体BELLA运行时间提高了10.6倍。最后，我们调整了LOGAN的Roofline模型，并证明我们的实现在NVIDIA Tesla V100上接近最佳。
英文摘要：
Pairwise sequence alignment is one of the most computationally intensive kernels in genomic data analysis, accounting for more than 90% of the runtime for key bioinformatics applications. This method is particularly expensive for third-generation sequences due to the high computational cost of analyzing sequences of length between 1Kb and 1Mb. Given the quadratic overhead of exact pairwise algorithms for long alignments, the community primarily relies on approximate algorithms that search only for high-quality alignments and stop early when one is not found. In this work, we present the first GPU optimization of the popular X-drop alignment algorithm, that we named LOGAN. Results show that our high-performance multi-GPU implementation achieves up to 181.6 GCUPS and speed-ups up to 6.6x and 30.7x using 1 and 6 NVIDIA Tesla V100, respectively, over the state-of-the-art software running on two IBM Power9 processors using 168 CPU threads, with equivalent accuracy. We also demonstrate a 2.3x LOGAN speed-up versus ksw2, a state-of-art vectorized algorithm for sequence alignment implemented in minimap2, a long-read mapping software. To highlight the impact of our work on a real-world application, we couple LOGAN with a many-to-many long-read alignment software called BELLA, and demonstrate that our implementation improves the overall BELLA runtime by up to 10.6x. Finally, we adapt the Roofline model for LOGAN and demonstrate that our implementation is near-optimal on the NVIDIA Tesla V100s.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
