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Updated: Jun 23, 2026
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Published on: December 22, 2017
Accelign: a GPU-based library for accelerating pairwise sequence alignment
Felix Kallenborn1, Fawaz Dabbaghie2, Martin Steinegger2
1Department of Computer Science, Johannes Gutenberg University, Mainz, 55099, Germany. kallenborn@uni-mainz.de.
BMC Bioinformatics
|June 21, 2026
Summary
Accelign offers accelerated pairwise sequence alignment on GPUs, significantly speeding up bioinformatics pipelines. This library enhances DNA, RNA, and protein sequence analysis, outperforming existing methods.
Area of Science:
- Bioinformatics
- Computational Biology
- High-Performance Computing
Background:
- Increasing sequence data necessitates faster core algorithms.
- Dynamic programming for pairwise sequence alignment is computationally intensive.
- Efficient GPU implementations are crucial for bioinformatics pipelines.
Purpose of the Study:
- To develop a library of accelerated pairwise sequence alignment algorithms for GPUs.
- To address the quadratic time complexity of dynamic programming alignment.
- To support various alignment types and sequence data.
Main Methods:
- Developed Accelign, a library for CUDA-enabled GPUs.
- Utilized a wavefront parallelization strategy adaptable to multiple dynamic programming algorithms.
- Implemented local, global, and semi-global alignments for genomic and protein sequences.
Main Results:
- Accelign achieves peak performance of 16.1 TCUPS and 9.1 TCUPS on a single GPU.
- Demonstrated significant speedups over CPU-based and existing GPU-based libraries.
- Showcased linear performance scaling with the number of GPUs.
Conclusions:
- Accelign provides substantial speedups for pairwise alignment algorithms.
- The library offers improved performance compared to prior implementations.
- Accelign is publicly available for use and further development.
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