gpuArray | Create array on GPU |
gather | Transfer distributed array or gpuArray to local workspace |
existsOnGPU | Determine if gpuArray or CUDAKernel is available on GPU |
gpuDevice | Query or select GPU device |
gpuDeviceCount | Number of GPU devices present |
gputimeit | Time required to run function on GPU |
reset | Reset GPU device and clear its memory |
wait (GPUDevice) | Wait for GPU calculation to complete |
arrayfun | Apply function to each element of array on GPU |
bsxfun | Binary singleton expansion function for gpuArray |
pagefun | Apply function to each page of array on GPU |
gpuArray | Array stored on GPU |
GPUDevice | Graphics processing unit (GPU) |
GPUDeviceManager | Manager for GPU Devices |
Identify and Select a GPU Device
Use gpuDevice to identify and select
which device you want to use.
A
Run Built-In Functions on a GPU
Many MATLAB built-in functions support
Run Element-wise MATLAB Code on GPU
Use examples to learn about running MATLAB code on a GPU
Improve Performance of Small Matrix Problems on the GPU using PAGEFUN
This example shows how to use pagefun 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
You can use various benchmark tests in MATLAB to measure the performance of your GPU:
By default, each worker in a cluster working on the same job has a unique random number stream.