Using GPUs to speed-up Levenshtein edit distance computation

Balhaf, Khaled; Shehab, Mohammed A.; Al-Sarayrah, Walaa; Al‐Ayyoub, Mahmoud; Al-Saleh, Mohammed I.; Jararweh, Yaser

doi:10.1109/iacs.2016.7476090

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…For example, Fakirah et al [28] utilized a diagonal traversing approach to enhance the Needleman-Wunsch algorithm by utilizing the iterations used to fill the scoring matrix. Balhaf et al [29] enhanced the Levenshtein edit distance algorithm's performance by using the diagonal traversing approach, and the performance was enhanced using both CPU and GPU. Jararweh et al [30] accelerated the Levenshtein and Damerau algorithms by using parallel implementation on a GPU.…”

Section: Pairwise Sequence Alignment (Psa)mentioning

confidence: 99%

Accelerating Multiple Sequence Alignments Using Parallel Computing

Bani Baker,

Al-Hussien,

Al-Ayyoub

2024

Computation

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Multiple sequence alignment (MSA) stands as a critical tool for understanding the evolutionary and functional relationships among biological sequences. Obtaining an exact solution for MSA, termed exact-MSA, is a significant challenge due to the combinatorial nature of the problem. Using the dynamic programming technique to solve MSA is recognized as a highly computationally complex algorithm. To cope with the computational demands of MSA, parallel computing offers the potential for significant speedup in MSA. In this study, we investigated the utilization of parallelization to solve the exact-MSA using three proposed novel approaches. In these approaches, we used multi-threading techniques to improve the performance of the dynamic programming algorithms in solving the exact-MSA. We developed and employed three parallel approaches, named diagonal traversing, blocking, and slicing, to improve MSA performance. The proposed method accelerated the exact-MSA algorithm by around 4×. The suggested approaches could be basic approaches to be combined with many existing techniques. These proposed approaches could serve as foundational elements, offering potential integration with existing techniques for comprehensive MSA enhancement.

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Section: Pairwise Sequence Alignment (Psa)mentioning

confidence: 99%

Accelerating Multiple Sequence Alignments Using Parallel Computing

Bani Baker,

Al-Hussien,

Al-Ayyoub

2024

Computation

Self Cite

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“…Additionally, there have been many efforts to adapt and optimise these algorithms on GPU devices. Most relevant proposals are based on DP, computing cells antidiagonal-wise in parallel [61,62,63,64,65]. Meanwhile, some research efforts have been focused on producing efficient CUDA implementation of the classical Needleman-Wunsch [66] algorithm; other proposals have focused on novel organisations of the DP-matrix to exploit efficiently the GPU resources [67].…”

Section: Related Workmentioning

confidence: 99%

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

Aguado-Puig¹,

Marco-Sola²,

Moure³

et al. 2022

IEEE Access

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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 editdistance 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× 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.

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“…This algorithm obtains the minimum cost for transforming one 80 string into another one using dynamic programming, which considerably reduces the time consumption of the process. Some other proposals for optimizing this dissimilarity are the use of general algorithm optimization techniques such as the Method of Four Russians [14] or solutions based on Graphic Processing Units (GPUs) together with parallel computing [15].…”

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confidence: 99%