Accelerating MATLAB with GPU Computing: A Primer with - download pdf or read online

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 usually calls for that MATLAB codes run swifter by means of leveraging the dispensed parallelism of photographs Processing devices (GPUs). whereas MATLAB effectively presents high-level features as a simulation software for quick prototyping, the underlying information and information 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, developing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then courses clients via complicated subject matters similar to CUDA libraries. The authors percentage their event constructing algorithms utilizing MATLAB, C++ and GPUs for big datasets, enhancing MATLAB codes to higher make the most of the computational strength of GPUs, and integrating them into advertisement software program items. in the course of the ebook, they show 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 site: http://booksite.elsevier.com/9780124080805/

• indicates the way to speed up MATLAB codes during the GPU for parallel processing, with minimum knowledge
• Explains the comparable historical past on undefined, structure and programming for ease of use
• offers uncomplicated labored examples of MATLAB and CUDA C codes in addition to templates that may be reused in real-world initiatives

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We then create the mex function in which we call our CUDA-based convolution function. h" void mexFunction(int nlhs, mxArray *plhs[], int nrhs, mxArray *prhs[]) { if (nrhs ! 5 2) mexErrMsgTxt("Invaid number of input arguments"); if (nlhs ! mxIsSingle(prhs[1])) mexErrMsgTxt("input image and mask type must be single"); Configurations for MATLAB and CUDA 41 float* image 5 (float*)mxGetData(prhs[0]); float* mask 5 (float*)mxGetData(prhs[1]); 15 16 17 18 19 20 21 22 23 24 25 26 int numRows 5 (int)mxGetM(prhs[0]); int numCols 5 (int)mxGetN(prhs[0]); int numKRows 5 (int)mxGetM(prhs[1]); int numKCols 5 (int)mxGetN(prhs[1]); if (numKRows !

3 Convolution in Custom c-mex with CUDA In this example, we use a CUDA function to do convolution operations. This CUDA function is functionally same as the c-mex function in previous section except for the CUDA implementation. Step 1. First, we define the function prototype we call in our CUDA function. h: 1 2 3 4 5 6 7 8 9 10 #ifndef __CONV2MEXCUDA_H__ #define __CONV2MEXCUDA_H__ extern void conv2Mex(float* in, float* out, int numRows, int numCols, float* mask); #endif // __CONV2MEXCUDA_H__ Step 2.

Configurations for MATLAB and CUDA 35 Step 1. Read the sample image of coins in the MATLAB command window: .. .. tif’)); mask 5 single([1 2 1; 0 0 0; -1 -2 -1]); imagesc(quarters); colormap(gray); Note that we cast the input image and mask to the single data type. When we read an image using imread, it returns the image in uint8 data type. Since we will work with a single data type in CUDA, we are preparing the input data as single. Step 2. Do two-dimensional convolution using conv2: .. H 5 conv2(quarters, mask, ‘same’); For now, we chose to do the convolution with the shape parameter, ‘same’ By specifying the third parameter same, we ask MATLAB to return the output of the same size as the input image.

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


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