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Advanced Topics GPU Programming and CUDA Architecture
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Author(s): Mainak Adhikari (IMPS College of Engineering and Technology, India)and Sukhendu Kar (IMPS College of Engineering and Technology, India)
Copyright: 2016
Pages: 29
Source title:
Emerging Research Surrounding Power Consumption and Performance Issues in Utility Computing
Source Author(s)/Editor(s): Ganesh Chandra Deka (Regional Vocational Training Institute (RVTI) for Women, India), G.M. Siddesh (Ramaiah Institute of Technology, India), K. G. Srinivasa (M S Ramaiah Institute of Technology, Bangalore, India)and L.M. Patnaik (IISc, Bangalore, India)
DOI: 10.4018/978-1-4666-8853-7.ch008
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Abstract
Graphics processing unit (GPU), which typically handles computation only for computer graphics. Any GPU providing a functionally complete set of operations performed on arbitrary bits can compute any computable value. Additionally, the use of multiple graphics cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing. CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs). CUDA gives program developers direct access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. This chapter first discuss some features and challenges of GPU programming and the effort to address some of the challenges with building and running GPU programming in high performance computing (HPC) environment. Finally this chapter point out the importance and standards of CUDA architecture.
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