Prospective Application of Parallelization to Accelerate Biological Model Simulations
Kishore G R1, Shubhamangala B R2
1Kishore G R, Information Science and Engineering, Jyothy Institute of Technology, Bengaluru, India.
2Dr.Shubhamangala B R, Professor, Researcher Head, Bengaluru, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 5195-5201 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7405118419/2019©BEIESP | DOI: 10.35940/ijrte.D7405.118419
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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: Biological systems can be modeled using mathematical techniques to carry out in silico experiments and research. These models tend to have a lot of state variables and hence take a long time to simulate the model. Modern GPU architecture provides a framework for parallelizing computation-heavy processes. With the advent of GPU technology, it is increasingly used in the field of computational biology, aiming to reduce simulation times and increase the size of inputs. This paper surveys the use of GPU architecture in the field of biological modeling..
Keywords: CUDA,GPU,CPU,GPGPUBCF,KARMA MODEL.
Scope of the Article: Computational Biology.