GPU Computing in MATLAB

Accelerate your code using basic GPU computing

Functions

Key Functions

`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`	查询或选择GPU设备
`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

Classes

Key Classes

`gpuArray`	Array stored on GPU
`GPUDevice`	图ics processing unit (GPU)
`GPUDeviceManager`	Manager for GPU Devices

Topics

Identify and Select a GPU Device

UsegpuDeviceto identify and select which device you want to use.

Establish Arrays on a GPU

AgpuArrayin MATLAB^®represents an array that is stored on the GPU.

Run Built-In Functions on a GPU

Many MATLAB built-in functions supportgpuArrayinput arguments.

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 usepagefunto 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

The purpose of GPU computing in MATLAB is to speed up your applications.

Control Random Number Streams

By default, each worker in a cluster working on the same job has a unique random number stream.