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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.

Accelerating MATLAB with GPU Computing

POST SECONDARY FOUR YEAR

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ACCELERATING MATLAB WITH GPU COMPUTING

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Contents.
Preface.
1: Accelerating MATLAB without GPU.
2: Configurations for MATLAB and CUDA.
3: Optimization Planning through Profiling.
4: CUDA Coding with c-mex.
5: MATLAB and Parallel Computing Toolbox.
6: Using CUDA-Accelerated Libraries.
7: Example in Computer Graphics.
8: CUDA Conversion Example: 3D Image Processing.
Appendix 1: Download and Install the CUDA Library.
Appendix 2: Installing NVIDIA Nsight Into Visual Studio.
Bibliography.
Index.

Accelerating MATLAB with GPU Computing.css-1p8rvzy{position:absolute;padding-left:8px;}

Accelerating MATLAB with GPU Computing