GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems.…mehr
GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development.
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Autorenporträt
Nikolaos Ploskas is a Postdoctoral Researcher at the Department of Chemical Engineering, Carnegie Mellon University, USA. He received his Bachelor of Science degree, Master's degree, and Ph.D. in Computer Systems from the Department of Applied Informatics of the University of Macedonia, Greece. His primary research interests are in:
Operations research,
Mathematical programming,
Linear programming,
Parallel programming,
GPU programming,
Decision support systems.
Dr. Ploskas has participated in several international and national research projects. He is the author or co-author of writings in more than 40 publications, including high-impact journals and book chapters, and conference publications. He has also served as a reviewer for many scientific journals. He received an honorary award from HELORS (Hellenic Operations Research Society) for the best doctoral dissertation in operations research (2014).
Inhaltsangabe
1. Introduction2. Getting started3. Parallel Computing Toolbox4. Introduction to GPU programming in MATLAB5. GPU programming on MATLAB toolboxes6. Multiple GPUs7. Run CUDA or PTX code8. MATLAB MEX functions containing CUDA code9. CUDA-accelerated libraries10. Profiling code and improving GPU performance
1. Introduction2. Getting started3. Parallel Computing Toolbox4. Introduction to GPU programming in MATLAB5. GPU programming on MATLAB toolboxes6. Multiple GPUs7. Run CUDA or PTX code8. MATLAB MEX functions containing CUDA code9. CUDA-accelerated libraries10. Profiling code and improving GPU performance
Rezensionen
"With GPU programming becoming commonplace, such a dedicated, detailed and highly readable book on this subject is a welcome addition. This textbook should be on the bookshelf of any MATLAB programmer who plans to employ GPU parallelization." --Yair Altman, author: Accelerating MATLAB Performance? (CRC Press, 2014), http://UndocumentedMatlab.com
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