Accelerating MATLAB with GPU Computing: A Primer with by Jung W. Suh, Youngmin Kim PDF

By Jung W. Suh, Youngmin Kim

ISBN-10: 0124080804

ISBN-13: 9780124080805

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 swifter by means of leveraging the dispensed parallelism of photographs Processing devices (GPUs). whereas MATLAB effectively offers high-level services as a simulation instrument for swift prototyping, the underlying info and data wanted for using GPUs make MATLAB clients hesitate to step into it. Accelerating MATLAB with GPUs bargains 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 complicated themes equivalent to CUDA libraries. The authors proportion their event constructing algorithms utilizing MATLAB, C++ and GPUs for big datasets, editing MATLAB codes to raised make the most of the computational strength of GPUs, and integrating them into advertisement software program items. in the course of the publication, they reveal many instance codes that may be used as templates of C-MEX and CUDA codes for readers' tasks. obtain instance codes from the publisher's web site:

• indicates the right way to speed up MATLAB codes in the course of 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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Extra resources for Accelerating MATLAB with GPU Computing: A Primer with Examples

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We then compile this code by running the following command in the command window, .. cpp The c-mex invokes our selected compiler to compile, link, and finally generate the binary, which we can call in our normal MATLAB session. Step 6. 6). Step 7. 7). 6 Creating a c-mex file from the C11 code. 7 Running Hello, mex in the command window. helloMex With just a couple of lines, we have just created our first C-MEX function! 1 Summary In this example, we created one special function called mexFunction.

Sh file for Mac OS X SDK selection. 4 Set a working directory as a current folder. Step 3. Open the MATLAB editor and create a new script by choosing File . New . Script from the menu. 5). Step 4. Type the following codes into the editor window and save the file by choosing File . 5 Save a new script as a C11 code. ) in C/C11. Unlike printing to stdout in pritnf, this prints your formatted message in the MATLAB command window. You will find its usage is same as printf. Step 5. Go back to the MATLAB.

5 3 jj numKCols ! 5 3) 44 mexErrMsgTxt("Invalid mask size. It must be 3 3 3"); 45 46 plhs[0] 5 mxCreateNumericMatrix(numRows, numCols, mxSINGLE_CLASS, mxREAL); 47 float* out 5 (float*)mxGetData(plhs[0]); 48 49 conv2Mex(image, out, numRows, numCols, mask); 50 } In mexFunction, we first check the number of inputs and outputs. In this example, there must be two inputs, image and mask, and one output, the convolution result. We then make sure that the input data type is single. We find out the size of the input image and the mask and make sure mask is of 3 3 3.

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Accelerating MATLAB with GPU Computing: A Primer with Examples by Jung W. Suh, Youngmin Kim

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