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 via leveraging the dispensed parallelism of pictures Processing devices (GPUs). whereas MATLAB effectively presents high-level capabilities as a simulation software for fast prototyping, the underlying info 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 courses clients via complicated issues akin to CUDA libraries. The authors percentage their adventure constructing algorithms utilizing MATLAB, C++ and GPUs for enormous datasets, enhancing MATLAB codes to higher make the most of the computational energy of GPUs, and integrating them into advertisement software program items. through the e-book, they display 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 how one can speed up MATLAB codes during the GPU for parallel processing, with minimum knowledge
• Explains the comparable heritage on undefined, structure and programming for ease of use
• offers easy labored examples of MATLAB and CUDA C codes in addition to templates that may be reused in real-world tasks

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

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Also, we tell the linker that we will be using CUDA runtime libraries and where they are located. Enter the following in the MATLAB command window. For Windows 64 bit, .. 0\lib\x64" For Windows 32 bit, .. 0\lib\Win32" For Linux, .. obj -lcudart -L"/usr/local/cuda/ lib" For MAC OSX, .. mexw64 for Windows 64 bit. 42 Accelerating MATLAB with GPU Computing Step 6. Execute our CUDA-based convolution function in the MATLAB command window: .. .. .. tif')); mask 5 single([1 2 1; 0 0 0; -1 -2 -1]); H3 5 conv2MexCuda(quarters, mask); imagesc(H3); colormap(gray); We should now see the same output image in the MATLAB figure.

H" void conv2Mex(float* src, float* dst, int numRows, int numCols, float* mask) { int boundCol 5 numCols - 1; int boundRow 5 numRows - 1; for (int c 5 1; c , boundCol; c11) { for (int r 5 1; r , boundRow - 1; r11) { int dstIndex 5 c * numRows 1 r; int kerIndex 5 8; for (int kc 5 -1; kc , 2; kc11) { int srcIndex 5 (c 1 kc) * numRows 1 r; Configurations for MATLAB and CUDA 17 18 37 for (int kr 5 -1; kr , 2; kr11) dst[dstIndex] 1 5 mask[kerIndex--] * src [srcIndex 1 kr]; } 19 20 } 21 } 22 } 23 24 void mexFunction(int nlhs, mxArray *plhs[], int nrhs, mxArray *prhs[]) 25 { 26 if (nrhs !

You can add the C11 compiler path to your system environment or pass it explicitly by using Àccbin option: .. 9 The created object file. Step 4. 9). Step 5. In this step, we create our mex function, which we call our AddVectors function. Just like our helloMex function, we start with mexFunction. Create a new file in the MATLAB editor. 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 vector data type must be single"); int numRowsA 5 (int)mxGetM(prhs[0]); int numColsA 5 (int)mxGetN(prhs[0]); int numRowsB 5 (int)mxGetM(prhs[1]); int numColsB 5 (int)mxGetN(prhs[1]); if (numRowsA !

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