Gpu Based Parallel Tlbo and Parallel Jaya for Multiple Sequence Alignment Using Mapreduce Gpu-Ptlbo & Gpu-Pjaya
Lakshmi Naga Jayaprada.Gavarraju1, K. Karteeka Pavan2, A. Deva Prema Swaroop3, Hemanth Chowdary Narne4
1Lakshmi Naga Jayaprada. Gavarraju, Assoc. Professor, Department of Computer Science & Engineering, Narasaraopeta Engineering College [Autonomous], Narasaraopet, Guntur (A.P), India.
2Kanadam Karteeka Pavan, Professor, Head Department of Computer Applications, R.V.R.& J.C.College of Engineering [Autonomous], Chowdavaram, Guntur (A.P), India.
3A. Deva Prema Swaroop, Assoc. Professor, Head Department of Computer Science, T.J.P.S.College, Guntur (A.P), India.
4Hemanth Chowdary Narne, Master’s in Applied Computer Science, North West Missouri State University, (Missouri), USA.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 250-259 | Volume-8 Issue-7, May 2019 | Retrieval Number: G5160058719/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Multiple sequence alignment (MSA) is an important issue in the field of bioinformatics. It is posed as an optimization problem by tuning the gaps to proper places that yields maximum alignment. Nature inspired evolutionary optimization algorithms are proven to be very powerful in wide range of optimization problems including multiple sequence alignment. In large data cases such as MSA, significantly more time is required for a reasonable search. Usage of multiple cores can lead to cover more search space in less time. This paper proposes two, Graphical Processor Unit (GPU) based parallel algorithms for MSA using recent algorithmic parameter free evolutionary algorithms Teaching Learning Based Optimization and JAYA (GPU-PTLBO & GPU-PJAYA) using mapreduce. The performance of the algorithms is evaluated by running on 16 different cores using well-known bench mark datasets. The results are compared with two other evolutionary algorithms parallel Genetic Algorithm and parallel Differential Evolutionary Algorithm. The results are profound in terms of accuracy and time. GPU-PTLBO has shown significant improvement over other algorithms. It was also observed that, GPU-PJAYA is efficient in case of short sequences.
Keyword: Multiple Sequence Alignment, Graphical Processor Unit, Search space, GA, DE, TLBO, JAYA, Mapreduce, Parallel Computing Toolbox, Distributed Computing Server.
Scope of the Article: Agent-Based Software Engineering.