Accelerating Edit-Distance Sequence Alignment on GPU Using the Wavefront Algorithm

IEEE Access

Santiago Marco-Sola¹  Lluc Alvarez¹  David Castells-Rufas²  Quim Aguado-Puig²  Miquel Moreto²  Juan Carlos Moure³  Antonio Espinosa³ 

[1] Arquitectura de Computadors i Sistemes Operatius, Universitat Aut&x00F2;Departament d&x2019;noma de Barcelona, Barcelona, Spain;

关键词: Approximate string matching;    compute unified device architecture (CUDA);    edit-distance;    graphics processing unit (GPU);    Levenshtein distance;    pairwise sequence alignment;   

DOI  :  10.1109/ACCESS.2022.3182714

来源: DOAJ

【 摘 要 】

Sequence alignment remains a fundamental problem with practical applications ranging from pattern recognition to computational biology. Traditional algorithms based on dynamic programming are hard to parallelize, require significant amounts of memory, and fail to scale for large inputs. This work presents eWFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute the exact edit-distance sequence alignment based on the wavefront alignment algorithm (WFA). This approach exploits the similarities between the input sequences to accelerate the alignment process while requiring less memory than other algorithms. Our implementation takes full advantage of the massive parallel capabilities of modern GPUs to accelerate the alignment process. In addition, we propose a succinct representation of the alignment data that successfully reduces the overall amount of memory required, allowing the exploitation of the fast shared memory of a GPU. Our results show that our GPU implementation outperforms by 3- $9\times $ the baseline edit-distance WFA implementation running on a 20 core machine. As a result, eWFA-GPU is up to 265 times faster than state-of-the-art CPU implementation, and up to 56 times faster than state-of-the-art GPU implementations.

【 授权许可】

Unknown