GPU Computing inMATLAB
Accelerate your code using basic GPU computing
To speed up your code, first try profiling and vectorizing it. For information, seePerformance and Memory. After profiling and vectorizing, you can also try using your computer’s GPU to speed up your calculations. If all the functions that you want to use are supported on the GPU, you can simply usegpuArray
to transfer input data to the GPU, and callgather
to retrieve the output data from the GPU. To get started with GPU computing, seeRun MATLAB Functions on a GPU.
For deep learning, MATLAB®provides automatic parallel support for multiple GPUs. SeeDeep Learning with MATLAB on Multiple GPUs(Deep Learning Toolbox).
Functions
Topics
RunMATLABCode on GPU
- Run MATLAB Functions on a GPU
Hundreds of functions in MATLAB and other toolboxes run automatically on a GPU if you supply agpuArray
argument. - Identify and Select a GPU Device
This example shows how to usegpuDevice
to identify and select which device you want to use. - GPU Support by Release
年代upport for NVIDIA®GPU architectures. - Establish Arrays on a GPU
AgpuArray
in MATLAB represents an array that is stored on the GPU. - Using FFT2 on the GPU to Simulate Diffraction Patterns
This example uses Parallel Computing Toolbox™ to perform a two-dimensional Fast Fourier Transform (FFT) on a GPU. - Run MATLAB Functions on Multiple GPUs
This example shows how to run MATLAB code on multiple GPUs in parallel, first on your local machine, then scaling up to a cluster. - Train Network Using Automatic Multi-GPU Support(Deep Learning Toolbox)
This example shows how to use multiple GPUs on your local machine for deep learning training using automatic parallel support.
Improve Performance on GPU
- Improve Performance of Element-wise MATLAB® Functions on the GPU using ARRAYFUN
This example shows howarrayfun
can be used to run a MATLAB® function natively on the GPU. - Improve Performance of Small Matrix Problems on the GPU using PAGEFUN
This example shows how to usepagefun
to improve the performance of applying a large number of independent rotations and translations to objects in a 3-D environment. - Measure and Improve GPU Performance
Use benchmark tests in MATLAB to measure the performance of your GPU. - Benchmarking A\b on the GPU
This example looks at how we can benchmark the solving of a linear system on the GPU.
Learn More
- Profile Your Code to Improve Performance
Use the Profiler to measure the time it takes to run your code and identify which lines of code consume the most time or which lines do not run. - Vectorization
Revise loop-based, scalar-oriented code to use MATLAB matrix and vector operations. - Random Number Streams on a GPU
Control the random number streams on a GPU to generate the same sequences of random numbers as on the CPU. - Generating Random Numbers on a GPU
This example shows how to switch between the different random number generators that are supported on the GPU. - 年代tencil Operations on a GPU
This example uses Conway's "Game of Life" to demonstrate how stencil operations can be performed using a GPU.