By Jung W. Suh, Youngmin Kim
Past simulation and set of rules improvement, many builders more and more use MATLAB even for product deployment in computationally heavy fields. This frequently calls for that MATLAB codes run speedier by way of leveraging the allotted parallelism of snap shots Processing devices (GPUs). whereas MATLAB effectively offers high-level services as a simulation software for speedy prototyping, the underlying information and data wanted for using GPUs make MATLAB clients hesitate to step into it. Accelerating MATLAB with GPUs deals a primer on bridging this gap.
Starting with the fundamentals, establishing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then publications clients via complex themes similar to CUDA libraries. The authors proportion their event constructing algorithms utilizing MATLAB, C++ and GPUs for big datasets, editing MATLAB codes to higher make the most of the computational energy of GPUs, and integrating them into advertisement software program items. during the publication, they display many instance codes that may be used as templates of C-MEX and CUDA codes for readers' initiatives. obtain instance codes from the publisher's web site: http://booksite.elsevier.com/9780124080805/
• exhibits find out how to speed up MATLAB codes throughout the GPU for parallel processing, with minimum knowledge
• Explains the comparable historical past on undefined, structure and programming for ease of use
• presents uncomplicated labored examples of MATLAB and CUDA C codes in addition to templates that may be reused in real-world tasks
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Past simulation and set of rules improvement, many builders more and more use MATLAB even for product deployment in computationally heavy fields. This frequently calls for that MATLAB codes run speedier by means of leveraging the dispensed parallelism of pictures Processing devices (GPUs). whereas MATLAB effectively presents high-level capabilities as a simulation instrument for fast prototyping, the underlying info and information wanted for using GPUs make MATLAB clients hesitate to step into it.
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Extra info for Accelerating MATLAB with GPU Computing: A Primer with Examples
To make the simplest c-mex example, Hello, c-mex. To configure CUDA for MATLAB. To make simple CUDA examples for MATLAB. 1 MATLAB Configuration for c-mex Programming Checklists MATLAB Executable (MEX) is intended to directly use C/C11 and FORTRAN codes within the MATLAB environment to accomplish higher executing speed and avoid application bottlenecks. We call C-MEX for the C/C11 MEX, and focus on C-MEX only in this book for the purpose of deploying a GPU device. Since c-mex requires building C/C11 executable and CUDA requires hardware-specific (NVIDIA GPU) codes, we need extra installation steps in addition to a standard MATLAB installation.
Employing the environment setting for the c-mex debugger. 2 MATLAB Code Profiling to Find Bottlenecks Fortunately, MATLAB provides a decent easy-to-use profiler. We are going to use the 2D convolution examples in the previous chapter again for profiling demonstrations. You can invoke the MATLAB profiler in two ways. 1. Second, simply type profile viewer in the command window. 2. 3). Then, type the command you want to run at Run This Code:. 4). Accelerating MATLAB with GPU Computing. 00003-1 © 2014 Elsevier Inc.
Step 6. To compile our mex and link to the CUDA object file we created, enter the following command in the MATLAB command window. 0, .. 0. For 32-bit Windows, replace x64 with Win32; for example, .. 0\lib\Win32" The -lcudart tells mex that we are using CUDA runtime libraries. 0\lib\x64" tells the location of those CUDA runtime libraries. For Mac OS X, .. 0/lib" And, for Linux Distributions, .. obj -lcudart -L"/usr/local/ cuda/lib" Step 7. 10). Step 8. Now, it is time to run our new mex function in the MATLAB.
Accelerating MATLAB with GPU Computing: A Primer with Examples by Jung W. Suh, Youngmin Kim