Abstract
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.
Highlights
This research paper proposes a novel dynamic programmingbased task scheduling for parallel multiple sequence alignment (MSA)
The application of dynamic programming optimization for task scheduling in case of parallelized multiple sequence alignment is preferred over other dynamic task scheduling approaches
The task scheduling during parallelization of MSA is based upon a single program and multiple data (SPMD) style
Summary
This research paper proposes a novel dynamic programmingbased task scheduling for parallel multiple sequence alignment (MSA). The application of dynamic programming optimization for task scheduling in case of parallelized multiple sequence alignment is preferred over other dynamic task scheduling approaches. The complex issue of task scheduling in any parallel processor system has multiple possible solutions. The structure of a problem like task scheduling can be characterized, the application of dynamic programming is the best solution. The same problems here mean the complete multiple alignment In this recursion, the intermediate results are stored in a matrix where they can be recalled later in the same program. Characterization of a problem is possible: like parallel MSA and task scheduling specification. Recursive definition of characterized problem is possible: In Parallel MSA task scheduling the same process takes place several times so recursion is important. Complex recursive and iterative greedy techniques further enhance the application of this hybrid methodology
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