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

Posted On April 11, 2017 at 5:48 pm by / Comments Off on Download Accelerating MATLAB with GPU Computing: A Primer with by Jung W. Suh, Youngmin Kim PDF

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 quicker by way of leveraging the disbursed parallelism of snap shots Processing devices (GPUs). whereas MATLAB effectively offers high-level features 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, developing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then publications clients via complicated issues similar to CUDA libraries. The authors proportion their event constructing algorithms utilizing MATLAB, C++ and GPUs for enormous datasets, enhancing MATLAB codes to higher make the most of the computational strength of GPUs, and integrating them into advertisement software program items. during the ebook, 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 site: http://booksite.elsevier.com/9780124080805/

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

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Additional info for Accelerating MATLAB with GPU Computing: A Primer with Examples

Example text

15 Select MATLAB as a connected application. 19). 20). Select the link, Lauches in the CUDA Overview title bar. 21). 22). 23). Once you are done, clos thee MATLAB window or click on Kill in Application Control. That closes down the whole session for profiling in Visual Studio. 2 CUDA Profiling Using NVIDIA Visual Profiler NVIDIA Visual Profiler provides a rich graphic user environment to give more insight into how CUDA works under the hood. In addition to giving us time profiles for each CUDA function call, it also tells us how the kernel was called, memory usage, and the like.

Step 4. 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 !

5 Example: Simple Vector Addition Using CUDA We start with very popular and simple example of vector addition. In this exercise, we create a CUDA function to add two input vectors of the same size and output the result to a separate output vector of the same size. Step 1. h in the working directory. Enter the following codes and save: 1 2 3 4 5 6 #ifndef __ADDVECTORS_H__ #define __ADDVECTORS_H__ extern void addVectors(float* A, float* B, float* C, int size); #endif // __ADDVECTORS_H__ In this header file, we declare our vector addition function prototype which we will use in our mex function.

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