Cover for Accelerating MATLAB with GPU Computing

Accelerating MATLAB with GPU Computing

A Primer with Examples

Book2014

Authors:

Jung W. Suh and Youngmin Kim

Accelerating MATLAB with GPU Computing

A Primer with Examples

Book2014

 

Cover for Accelerating MATLAB with GPU Computing

Authors:

Jung W. Suh and Youngmin Kim

About the book

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Book description

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB cod ... read full description

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  2. Book chapterAbstract only

    1 - Accelerating MATLAB without GPU

    Pages 1-17

  3. Book chapterAbstract only

    2 - Configurations for MATLAB and CUDA

    Pages 19-44

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    3 - Optimization Planning through Profiling

    Pages 45-72

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    4 - CUDA Coding with c-mex

    Pages 73-97

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    5 - MATLAB and Parallel Computing Toolbox

    Pages 99-125

  7. Book chapterAbstract only

    6 - Using CUDA-Accelerated Libraries

    Pages 127-155

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    7 - Example in Computer Graphics

    Pages 157-191

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    8 - CUDA Conversion Example: 3D Image Processing

    Pages 193-231

  10. Book chapterNo access

    Appendix 1 - Download and Install the CUDA Library

    Pages 233-238

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    Appendix 2 - Installing NVIDIA Nsight into Visual Studio

    Pages 239-241

  12. Book chapterNo access

    Bibliography

    Pages 243-244

  13. Book chapterNo access

    Index

    Pages 245-248

About the book

Description

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap.

Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers’ projects. Download example codes from the publisher's website: https://elsevier-booksite.publicaciones.saludcastillayleon.es/9780124080805/

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap.

Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers’ projects. Download example codes from the publisher's website: https://elsevier-booksite.publicaciones.saludcastillayleon.es/9780124080805/

Key Features

  • Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge
  • Explains the related background on hardware, architecture and programming for ease of use
  • Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects
  • Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge
  • Explains the related background on hardware, architecture and programming for ease of use
  • Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects

Details

ISBN

978-0-12-408080-5

Language

English

Published

2014

Copyright

Copyright © 2014 Elsevier Inc. All rights reserved.

Imprint

Morgan Kaufmann

Authors

Jung W. Suh

Youngmin Kim