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arXiv · 2208.01243

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

Safaa Diab·Amir Nassereldine·Mohammed Alser·Juan Gómez-Luna·Onur Mutlu·Izzat El Hajj
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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.

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Safaa Diab, Amir Nassereldine, Mohammed Alser, Juan Gómez-Luna, Onur Mutlu, Izzat El Hajj. 2022-08-02. A Framework for High-throughput Sequence Alignment using Real Processing-in-Memory Systems. https://arxiv.org/abs/2208.01243

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