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 swifter by way of leveraging the disbursed parallelism of photos Processing devices (GPUs). whereas MATLAB effectively offers high-level capabilities as a simulation device for speedy 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, establishing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then courses clients via complex subject matters resembling CUDA libraries. The authors percentage their adventure constructing algorithms utilizing MATLAB, C++ and GPUs for enormous datasets, enhancing MATLAB codes to raised make the most of the computational energy of GPUs, and integrating them into advertisement software program items. during the ebook, they exhibit 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/
• indicates find out how to speed up MATLAB codes during the GPU for parallel processing, with minimum knowledge
• Explains the comparable history 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
Read or Download Accelerating MATLAB with GPU Computing: A Primer with Examples PDF
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Additional resources for Accelerating MATLAB with GPU Computing: A Primer with Examples
MxIsSingle(prhs)) mexErrMsgTxt("input vector data type must be single"); int numRowsA 5 (int)mxGetM(prhs); int numColsA 5 (int)mxGetN(prhs); int numRowsB 5 (int)mxGetM(prhs); int numColsB 5 (int)mxGetN(prhs); if (numRowsA ! 5 numRowsB jj numColsA ! 5 numColsB) mexErrMsgTxt("Invalid size. The sizes of two vectors must be same"); int minSize 5 (numRowsA , numColsA) ? numRowsA : numColsA; int maxSize 5 (numRowsA . numColsA) ? numRowsA : numColsA; if (minSize ! 5 1) mexErrMsgTxt("Invalid dimentional"); size.
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. In the command window, run .. A 5 single([1 2 3 4 5 6 7 8 9 10]); .. B 5 single([10 9 8 7 6 5 4 3 2 1]); .. 10 The c-mex file created, Step 9.
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. In the command window, run .. A 5 single([1 2 3 4 5 6 7 8 9 10]); .. B 5 single([10 9 8 7 6 5 4 3 2 1]); .. 10 The c-mex file created, Step 9.