Faster and Cheaper: Pushing the Sequence Alignment Throughput with Commercial CPUs

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Authors: Zhonghai Zhang, Yewen Li, Ke Meng, Chunming Zhang, Guangming Tan
Cited by: 1
References: 43

Abstract

This paper proposes FastAlign, a faster, cheaper, and practical end-to-end solution for sequence alignment using commercial CPUs. It introduces two key innovations: a multi-stage seeding algorithm that improves search performance while maintaining low memory consumption, and an intra-query parallel seed-extension algorithm that eliminates redundancy and increases SIMD utilization. Evaluation results show that FastAlign achieves 2.27× ∼ 3.28× throughput speedup and 2.54× ∼ 5.65× cost reduction compared to state-of-the-art CPU and GPU baselines while guaranteeing 100% identical output to the de facto software BWA-MEM. FastAlign is open-sourced at https://github.com/zzhofict/BWA-FastAlign.git.

Fields of Study & Subjects

Genomics and Phylogenetic Studies Network Packet Processing and Optimization Parallel Computing and Optimization Techniques

Publication details

Journal / source
Proceedings of the 31st ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming
Publisher
ACM
Pages
466–479
Publication date
28 January 2026