Distributed Algorithm for Parallel Edit Distance Computation

Sadiq, Muhammad Umair; Yousaf, Muhammad Murtaza

doi:10.31577/cai_2020_4_757

Cited by 6 publications

(7 citation statements)

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“…To base our experiments, we use the test files used by [23] and the CUDAlign project publications [26,28] which cover enough differences in terms of string similarity and length. In the case of [23] the files are not exactly the same reported in the original paper as they were the result of concatenating multiple genomic sequences combined in a .seq file, and it has been evolving over time. We have repeated the process with the same sequences files and similar string lengths.…”

Section: Resultsmentioning

confidence: 99%

“…Method/Platform Max reported length Max performance (GCUPS) [22] The Table 2 compares the results of the proposed method in execution platform with Sadiq and Yousaf's results reported in the paper [23]. The input strings have a rather low similarity below 50%, but Sadiq and Yousaf's top performance is below 2 GCUPS.…”

Section: Workmentioning

confidence: 97%

“…Computer clusters have not been generally used to speed up this problem. Sadiq and Yousaf [23] is one of the few works trying to parallelize ED for clusters with modest results.…”

Section: Cupsmentioning

confidence: 99%

See 2 more Smart Citations

Gpu Acceleration of Levenshtein Distance Computation between Long Strings

Castells-Rufas

2022

SSRN Journal

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Section: Resultsmentioning

confidence: 99%

Section: Workmentioning

confidence: 97%

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Gpu Acceleration of Levenshtein Distance Computation between Long Strings

Castells-Rufas

2022

SSRN Journal

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“…Sadiq and Yousaf 28 presents a distributed algorithm for parallel edit distance/Levenshtein distance computation between two strings. The proposed algorithm is both time and space efficient, and ensures balanced workload among processors.…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

A scalable parallel algorithm for global sequence alignment with customizable scoring scheme

Sadiq,

Yousaf

2023

Concurrency and Computation

Self Cite

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SummarySequence alignment is a critical computational problem in various domains, including genomics, proteomics, and natural language processing. The Needleman‐Wunsch (NW) algorithm is a classical dynamic programming approach for finding the optimal global alignment between two sequences. However, its quadratic time and space complexity make it impractical for aligning large‐scale sequences, which are increasingly common in modern applications. In this article, we propose a parallel variation of the NW algorithm that enables scalable global sequence alignment with customizable scoring schemes. Our approach re‐formulates the dependencies in the NW algorithm to enable parallel execution, thereby leveraging the computational power of modern parallel architectures, such as graphics processing unit (GPU). Furthermore, our algorithm supports arbitrary linear scoring schemes, which allows us to use domain‐specific knowledge to improve alignment accuracy. We establish the correctness of our algorithm and evaluate its performance using real DNA and user trajectory sequences on GPUs. Our parallel algorithm has shown impressive results in our experiments, with a peak performance of 27.99 GCUPS (giga cell updates per second) and a maximum speedup of 48.18 times compared to the traditional sequential implementation. Additionally, our algorithm demonstrates remarkable scalability, enabling the alignment of sequences of any length while ensuring balanced work distribution and optimal utilization of resources. Our primary objective is to harness the computational capabilities of a single GPU and fully utilize the processing power of multi‐core CPUs.

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“…A graph is represented as G = (V, E) where V is the set of vertices, and E is the set of edges (Sadiq & Yousaf, 2020;Chu & Wu, 2021;Losqui & Souza, 2019). According to Arranz (2015) and…”

Section: Graph and Distance Calculationmentioning

confidence: 99%

Parallelization of Shortest Path Class Algorithms: A Comparative Analysis

Souza,

Abreu,

Trindade

et al. 2023

Pesqui. Oper.

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The problem of finding the shortest path between a source and a destination node, commonly represented by graphs, has several computational algorithms as an attempt to find what is called the minimum path. Depending on the number of nodes in-between the source and destination, the process of finding the shortest path can demand a high computational cost (with polynomial complexity). A solution to reduce the computational cost is the use of the concept of parallelism, which divides the algorithm tasks between the processing cores. This article presents a comparative analysis of the main algorithms of the shortest path class: Dijkstra, Bellman-Ford, Floyd-Warshall and Johnson. The performance of each algorithm was evaluated considering different parallelization approaches and they were applied on general and open-pit mining databases present in the literature. The experimental results showed an improvement in performance of about 55% on the execution time depending on the chosen parallelization point.

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Distributed Algorithm for Parallel Edit Distance Computation

Cited by 6 publications

References 0 publications

Gpu Acceleration of Levenshtein Distance Computation between Long Strings

Gpu Acceleration of Levenshtein Distance Computation between Long Strings

A scalable parallel algorithm for global sequence alignment with customizable scoring scheme

Parallelization of Shortest Path Class Algorithms: A Comparative Analysis

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