Department of Computer Science, American University of Beirut, Riad El-Solh, Beirut 1107 2020, Lebanon.
Department of Information Technology and Electrical Engineering, ETH Zürich, Gloriastrasse 35, Zürich 8092, Switzerland.
Bioinformatics. 2023 May 4;39(5). doi: 10.1093/bioinformatics/btad155.
Sequence alignment is a memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory (PIM) architectures alleviate this bottleneck by providing the memory with computing competencies. We propose Alignment-in-Memory (AIM), a framework for high-throughput sequence alignment using PIM, and evaluate it on UPMEM, the first publicly available general-purpose programmable PIM system.
Our evaluation shows that a real PIM system can substantially outperform server-grade multi-threaded CPU systems running at full-scale when performing sequence alignment for a variety of algorithms, read lengths, and edit distance thresholds. We hope that our findings inspire more work on creating and accelerating bioinformatics algorithms for such real PIM systems.
Our code is available at https://github.com/safaad/aim.
序列比对是一种受内存限制的计算，其在现代系统中的性能受内存带宽瓶颈的限制。基于内存处理（PIM）架构通过为内存提供计算能力来缓解这一瓶颈。我们提出了 Alignment-in-Memory（AIM），这是一个使用 PIM 进行高通量序列比对的框架，并在第一个公开可用的通用可编程 PIM 系统 UPMEM 上对其进行了评估。
我们的评估表明，在针对各种算法、读取长度和编辑距离阈值执行序列比对时，实际的 PIM 系统在性能上可以大大超过全规模运行的服务器级多线程 CPU 系统。我们希望我们的发现能够激发更多针对这种实际 PIM 系统的生物信息学算法的创建和加速工作。
我们的代码可在 https://github.com/safaad/aim 上获得。
