SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

TL;DR

SaLoBa is a GPU-accelerated sequence alignment library that enhances data locality and workload balance, leading to significant performance improvements in seed extension tasks for sequencing applications.

Contribution

The paper introduces SaLoBa, a novel GPU-based sequence alignment library that optimizes data locality and workload distribution, addressing limitations of previous GPU methods.

Findings

SaLoBa outperforms existing GPU-based sequence alignment methods.

Significant speedup in seed extension kernel performance.

Effective exploitation of data locality and workload balance.

Abstract

Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensional dynamic programming approach. Although some prior work has been conducted on GPU acceleration of a sequence alignment, we identify several shortcomings that limit exploiting the full computational capability of modern GPUs. This paper presents SaLoBa, a GPU-accelerated sequence alignment library focused on seed extension. Based on the analysis of previous work with real-world sequencing data, we propose techniques to exploit the data locality and improve workload balancing. The experimental results reveal that SaLoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods.