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Reseach Article

Improving MSAProbs Algorithm performance and Parallel Computing using GPU

by Sally Zaki El-hadary, Sara A. Shehab, Hatem Said Ahmed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 1
Year of Publication: 2023
Authors: Sally Zaki El-hadary, Sara A. Shehab, Hatem Said Ahmed
10.5120/ijca2023922532

Sally Zaki El-hadary, Sara A. Shehab, Hatem Said Ahmed . Improving MSAProbs Algorithm performance and Parallel Computing using GPU. International Journal of Computer Applications. 185, 1 ( Apr 2023), 14-18. DOI=10.5120/ijca2023922532

@article{ 10.5120/ijca2023922532,
author = { Sally Zaki El-hadary, Sara A. Shehab, Hatem Said Ahmed },
title = { Improving MSAProbs Algorithm performance and Parallel Computing using GPU },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2023 },
volume = { 185 },
number = { 1 },
month = { Apr },
year = { 2023 },
issn = { 0975-8887 },
pages = { 14-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number1/32668-2023922532/ },
doi = { 10.5120/ijca2023922532 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:25:05.474779+05:30
%A Sally Zaki El-hadary
%A Sara A. Shehab
%A Hatem Said Ahmed
%T Improving MSAProbs Algorithm performance and Parallel Computing using GPU
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 1
%P 14-18
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

MSA Probs is a parallel algorithm developed to align multiple sequence alignment using a central processing unit (CPU). Whereas the CPU has some limitations, such as the inability to parallelize tasks in the processor (latency-oriented). To overcome these limitations, this paper proposes an improved version of MSA Probs that is compatible with a graphical Processing Unit (GPU). This idea helps in enhancing the performance of our algorithm (MSAprobs). To parallelize the sequential algorithm, Compute Unified Device Architecture (CUDA) or OpenCL is commonly used on GPUs. The NIVIDIA API is used to investigate the GPU's computing power. The results of using CPU only in MSAprobs versus the CPU and GPU are compared using two data sets from the Bali Base and OX Bench. The evaluation of the CPU and GPU is done using Threads 1,2 and 4. The results showed that by combining CPU and GPU, performance is improved and execution time is reduced.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Multiple sequence alignment parallel processing Latency Oriented GPU CPU