A Framework for High-throughput Sequence Alignment using Real Processing-in-Memory Systems

TL;DR

This paper introduces AIM, a processing-in-memory framework that significantly accelerates sequence alignment tasks, outperforming traditional CPU systems by leveraging in-memory computing capabilities.

Contribution

The paper presents AIM, the first framework for high-throughput sequence alignment using real processing-in-memory hardware, demonstrating substantial performance improvements.

Findings

Processing-in-memory systems outperform CPU systems in sequence alignment tasks.

AIM achieves high throughput across various algorithms and read lengths.

Real PIM hardware can effectively accelerate bioinformatics computations.

Abstract

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.