Parallelization of Data Intensive Code Using Computer Unified Device Architecture (CUDA)

Bhardwaj Aditi, Bhardwaj Rohatash K., Shishir K. Gangwar
College of Technology, Govind Ballabh Pant University of Agriculture & Technology, Pantnagar
International Journal of Engineering and Management Sciences (IJEMS), Vol3(3), 2012


   title={Parallelization of Data Intensive Code Using Computer Unified Device Architecture (CUDA)},

   author={Aditi, Bhardwaj and Rohatash, Bhardwaj K. and Gangwar, Shishir K.},



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Parallel processing is a form of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently. Parallelism has been employed for many years, mainly in high-performance computing. As power consumption by Computer has become a concern in recent years, parallel computing has become the dominant paradigm in Computer structural design, mainly in the form of multi-core processors. Parallel Computer programs are more difficult to write than sequential ones. CUDA (an acronym for COMPUTER Unified Device Architecture) is a parallel computing architecture developed by NVIDIA. CUDA gives developers access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. Using CUDA, the latest NVIDIA GPUs become accessible for computation like CPUs.GPU computing or GPGPU is the use of a GPU (graphics processing unit) to do general purpose scientific and engineering computing. RNA is made up of a long chain of components called nucleotides. Each nucleotide consists of a a nitrogenous base, a ribose sugar, and a phosphate group. The sequence of nucleotides allows RNA to encode genetic information. The chemical structure of RNA is very similar to that of DNA, with two differences (a) RNA contains the sugar ribose while DNA contains the slightly different sugar deoxyribose (b) RNA has the uracil while DNA contains thymine. In this an attempt is being made to COMPUTER RNA Secondary structure algorithm in parallel using CUDA architecture, COMPUTER the time of execution on CPU and GPU. And compare the different RNA sequences for further scientific advancements.
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