An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences


Ishaq, Muhammad and Khan, Asfandyar and Mohd Su'ud, Mazliham and Alam, Muhammad Mansoor and Bangash, Javed Iqbal and Khan, Abdullah (2022) An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences. Computational and Mathematical Methods in Medicine, 2022. pp. 1-11. ISSN 1748-670X

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Task scheduling in parallel multiple sequence alignment (MSA) through improved dynamic programming optimization speeds up alignment processing. The increased importance of multiple matching sequences also needs the utilization of parallel processor systems. This dynamic algorithm proposes improved task scheduling in case of parallel MSA. Specifically, the alignment of several tertiary structured proteins is computationally complex than simple word-based MSA. Parallel task processing is computationally more efficient for protein-structured based superposition. The basic condition for the application of dynamic programming is also fulfilled, because the task scheduling problem has multiple possible solutions or options. Search space reduction for speedy processing of this algorithm is carried out through greedy strategy. Performance in terms of better results is ensured through computationally expensive recursive and iterative greedy approaches. Any optimal scheduling schemes show better performance in heterogeneous resources using CPU or GPU.

Item Type: Article
Uncontrolled Keywords: Dynamic programming
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 03 Mar 2022 02:49
Last Modified: 14 Dec 2022 07:55


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