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

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 speedier through leveraging the disbursed parallelism of pictures Processing devices (GPUs). whereas MATLAB effectively presents high-level services as a simulation software for speedy prototyping, the underlying info and information 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, constructing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then courses clients via complicated subject matters equivalent to CUDA libraries. The authors proportion their adventure constructing algorithms utilizing MATLAB, C++ and GPUs for enormous datasets, editing MATLAB codes to raised make the most of the computational energy of GPUs, and integrating them into advertisement software program items. through the publication, 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 tips 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 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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Extra resources for Accelerating MATLAB with GPU Computing: A Primer with Examples

Example text

Once you identify those locations and set their paths in your system environment accordingly, your sail through C-MEX and CUDA programming will be a lot easier and smoother. 2 Compiler Selection We begin by selecting our C compiler from MATLAB. In MATLAB command window, run mex -setup. 1). In this example, two Microsoft Visual C11 compilers are available; we choose Microsoft Visual C11 2010 as our C11 compiler by selecting [1]. 1 The c-mex configuration message in MATLAB command window. MATLAB asks you to verify your choice.

5 3) mexErrMsgTxt("Invalid mask size. It must be 3 3 3"); 27 28 29 30 } plhs[0] 5 mxCreateNumericMatrix(numRows, numCols, mxSINGLE_CLASS, mxREAL); float* out 5 (float*)mxGetData(plhs[0]); conv2Mex(image, out, numRows, numCols, mask); Our new mex function is almost the same as we had in a previous section. h” on line 1. cu. Step 5. We are ready to make our CUDA-based mex function. obj, so we have to tell our linker where that function is. Also, we tell the linker that we will be using CUDA runtime libraries and where they are located.

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. extern indicates that our function is implemented in some other file. Step 2. cu. cu represents the CUDA file. Create a new file in the MATLAB editor. x; 7 if (i . 5 size) 8 return; 9 10 C[i] 5 A[i] 1 B[i]; Configurations for MATLAB and CUDA 29 11 } 12 13 void addVectors(float* A, float* B, float* C, int size) 14 { 15 float *devPtrA 5 0, *devPtrB 5 0, *devPtrC 5 0; 16 17 cudaMalloc(&devPtrA, sizeof(float) * size); 18 cudaMalloc(&devPtrB, sizeof(float) * size); 19 cudaMalloc(&devPtrC, sizeof(float) * size); 20 21 cudaMemcpy(devPtrA, A, sizeof(float) * size, cudaMemcpyHostToDevice); 22 cudaMemcpy(devPtrB, B, sizeof(float) * size, cudaMemcpyHostToDevice); 23 24 addVectorsMask ,, , size, 1 ..

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