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.
@article{arxiv.2208.01243,
title = {A Framework for High-throughput Sequence Alignment using Real Processing-in-Memory Systems},
author = {Safaa Diab and Amir Nassereldine and Mohammed Alser and Juan Gómez-Luna and Onur Mutlu and Izzat El Hajj},
journal= {arXiv preprint arXiv:2208.01243},
year = {2023}
}