GPU CUDA and MEX Programming

Further accelerate your code using advanced GPU CUDA® and MEX programming

If running MATLAB® functions on the GPU does not sufficiently speed up your code, or if you need to use advanced GPU CUDA features, you can write your own CUDA code and run it in MATLAB by generating an executable MEX file using mexcuda or an executable kernel using parallel.gpu.CUDAKernel.

Generally, using MEX files is more flexible than using CUDAKernel objects. For more information, see Run MEX Functions Containing CUDA Code.

Functions

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mexcudaCompile MEX functions or PTX files for GPU computation
CUDAKernelKernel executable on GPU
fevalEvaluate kernel on GPU
setConstantMemorySet some constant memory on GPU
mxGPUArrayType for MATLAB gpuArray
mxGPUCopyFromMxArrayCopy mxArray to mxGPUArray
mxGPUCopyGPUArrayDuplicate (deep copy) mxGPUArray object
mxGPUCopyImag Copy imaginary part of mxGPUArray
mxGPUCopyReal Copy real part of mxGPUArray
mxGPUCreateComplexGPUArrayCreate complex GPU array from two real gpuArrays
mxGPUCreateFromMxArrayCreate read-only mxGPUArray object from input mxArray
mxGPUCreateGPUArrayCreate mxGPUArray object, allocating memory on GPU
mxGPUCreateMxArrayOnCPUCreate mxArray for returning CPU data to MATLAB with data from GPU
mxGPUCreateMxArrayOnGPUCreate mxArray for returning GPU data to MATLAB
mxGPUDestroyGPUArrayDelete mxGPUArray object
mxGPUGetClassIDmxClassID associated with data on GPU
mxGPUGetComplexityComplexity of data on GPU
mxGPUGetDataRaw pointer to underlying data
mxGPUGetDataReadOnlyRead-only raw pointer to underlying data
mxGPUGetDimensionsmxGPUArray dimensions
mxGPUGetNumberOfDimensionsSize of dimension array for mxGPUArray
mxGPUGetNumberOfElementsNumber of elements on GPU for array
mxGPUIsSameDetermine if two mxGPUArrays refer to same GPU data
mxGPUIsSparseDetermine if mxGPUArray contains sparse GPU data
mxGPUIsValidGPUDataDetermine if mxArray is pointer to valid GPU data
mxGPUSetDimensionsModify number of dimensions and size of each dimension
mxIsGPUArrayDetermine if mxArray contains GPU data
mxInitGPUInitialize MATLAB GPU library on currently selected device

Topics

  • Run MEX Functions Containing CUDA Code

    If running MATLAB functions on the GPU does not sufficiently speed up your code, or if you need to use advanced GPU CUDA features, you can write your own CUDA code and run it in MATLAB by generating an executable MEX file using mexcuda.

  • Run CUDA or PTX Code on GPU

    If running MATLAB functions on the GPU does not sufficiently speed up your code, or if you need to use advanced GPU CUDA features, you can write your own CUDA code and run it in MATLAB by an executable kernel using parallel.gpu.CUDAKernel.

Featured Examples

Accessing Advanced CUDA Features Using MEX

Accessing Advanced CUDA Features Using MEX

How advanced features of the GPU can be accessed using MEX files. It builds on the example Stencil Operations on a GPU. The previous example uses Conway's "Game of Life" to demonstrate how stencil operations can be performed using MATLAB® code that runs on a GPU. The present example demonstrates how you can further improve the performance of stencil operations using an advanced feature of the GPU: shared memory. You do this by writing your own CUDA® code in a MEX file and calling the MEX file from MATLAB. You can find an introduction to the use of the GPU in MEX files in Run MEX-Functions Containing CUDA Code.

Illustrating Three Approaches to GPU Computing: The Mandelbrot Set

Illustrating Three Approaches to GPU Computing: The Mandelbrot Set

Adapt your MATLAB® code to compute the Mandelbrot Set using a GPU.