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Improving MSAProbs Algorithm performance and Parallel Computing using GPU

by Sally Zaki El-hadary, Sara A. Shehab, Hatem Said Ahmed
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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
  1. Yongchao Liu 1, Bertil Schmidt, Douglas L Maskell,”MSAProbs: multiple sequence alignment based on pair hidden Markov models and partition function posterior probabilities.n”,2010.
  2. Budd, Aidan , "Multiple sequence alignment exercises and demonstrations". European Molecular Biology Laboratory. Archived from the original on 5 March 2012.
  3. Yongchao Liu and Bertil Schmidt ,”Multiple Protein Sequence Alignment with MSAProbs”,2014.
  4. Silberstein, Mark; Schuster, Assaf; Geiger, Dan; Patney, Anjul; Owens, John D. , "Efficient computation of sum-products on GPUs through software-managed cache" ,2008.
  5. Nickolls, J., Dally, W.J., GPU Computing era“, IEEE Micro, vol. 30, No. 2, 2010., pp. 56–69.
  6. Abi-Chahla, Fedy , "Nvidia's CUDA: The End of the CPU?". Tom's Hardware. Retrieved May 17, 2015.
  7. “Nvidia CUDA C Best Practices Guide“, version 4.0, NVIDIA Corporaton, 2011.
  8. Sanders, J., Kandrot, E.,”CUDA by Example: An ntroduction to General-Purpose GPU Programming“, Addison-Wesley, 2010.
  9. “ NVIDIA CUDA C Programming Guide“, v rsion 4.0, NVIDIA Corporaton, 2011.
  10. An Overview of Multiple Sequence Alignment Systems Article February 2009 Fahad Saeed & Ashfaq A. Khokhar.
  11. Multiple sequence alignment modeling: methods and applications November 2015 Briefings in Bioinformatics 2015(6) Carsten Kemena , Jia-Ming Chang, Cedrik Magis&Maria Chatzou
Index Terms

Computer Science
Information Sciences

Keywords

Multiple sequence alignment parallel processing Latency Oriented GPU CPU

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