论文标题
使用内存系统真实处理的高通量序列对准的框架
A Framework for High-throughput Sequence Alignment using Real Processing-in-Memory Systems
论文作者
论文摘要
序列对齐是一个内存绑定的计算，其在现代系统中的性能受到内存带宽瓶颈的限制。内存架构的处理通过为记忆提供计算能力来减轻这种瓶颈。我们提出了记忆中的对齐（AIM），这是使用内存处理处理的高通量序列对齐框架，并在upmem上进行评估，upmem是第一个公共可用的通用通用可编程处理系统中的可音机处理。 我们的评估表明，在执行各种算法，读取长度和编辑距离阈值时，将在全尺度上以全面规模运行的真实处理系统可以大大优于服务器级的多线CPU系统。我们希望我们的发现激发了更多关于为这种内存系统进行实际处理的生物信息学算法创建和加速生物信息学算法的工作。 我们的代码可在https://github.com/safaad/aim上找到。
Sequence alignment is a memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory 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 processing-in-memory, and evaluate it on UPMEM, the first publicly-available general-purpose programmable processing-in-memory system. Our evaluation shows that a real processing-in-memory 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 processing-in-memory systems. Our code is available at https://github.com/safaad/aim.
