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GPU Programming in MATLAB

Autor Nikolaos Ploskas, Nikolaos Samaras
en Limba Engleză Paperback – 27 iul 2016
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.


  • Provides in-depth, comprehensive coverage of GPUs with MATLAB, including the parallel computing toolbox and built-in features for other MATLAB toolboxes
  • Explains how to accelerate computationally heavy applications in MATLAB without the need to re-write them in another language
  • Presents case studies illustrating key concepts across multiple fields
  • Includes source code, sample datasets, and lecture slides
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Specificații

ISBN-13: 9780128051320
ISBN-10: 0128051329
Pagini: 318
Dimensiuni: 191 x 235 x 15 mm
Greutate: 0.66 kg
Editura: ELSEVIER SCIENCE

Cuprins

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

Recenzii

"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