Run MATLAB Functions on a GPU
You can speed up your code by running MATLAB^{®} functions on a GPU. GPU computing in MATLAB requires Parallel Computing Toolbox™.
MATLAB Functions with gpuArray
Arguments
Many functions in MATLAB and other toolboxes run automatically on a GPU if you supply a gpuArray
data argument. A gpuArray
in MATLAB represents an array that is stored on the GPU.
A = gpuArray([1 0 1; 1 2 0; 0 1 1]); e = eig(A);
Whenever you call any of these functions with at least one gpuArray
as a data input argument, the function executes on the GPU. The function generates a gpuArray
as the result, unless returning numeric data to the local workspace is more appropriate (for example, size
). You can mix inputs using both gpuArray
data and arrays stored in host memory in the same function call. gpuArray
enabled functions include the discrete Fourier transform (fft
), matrix multiplication (mtimes
), left matrix division (mldivide
), and hundreds of others.
Conditions for gpuArray
inputs
GPUenabled functions run on the GPU only when the input data is on the GPU. The data type of parameter arguments such as dimensions or indices do not affect where the function is run. For example, the sum
function in this code runs on the GPU because the data, the first input, is on the GPU.
A = rand(10); d = 2; sum(gpuArray(A),d);
sum
function in this code does not run on GPU because the data, the first input, is not on the GPU.A = rand(10); d = 2; sum(A,gpuArray(d));
Work with Complex Numbers on a GPU
If the output of a function running on a GPU could potentially be complex, you must explicitly specify its input arguments as complex. For more information, see Work with Complex Numbers on a GPU.
Work with Sparse Arrays on a GPU
The sparse
function can be used to create sparse gpuArray
objects. Many MATLAB functions support sparse gpuArray
objects. For more information, see Work with Sparse Arrays on a GPU.
Check gpuArray
Supported Functions
Several MATLAB toolboxes include functions with gpuArray
support. To view
lists of all functions in these toolboxes that support gpuArray
objects, use
the links in the following table. Functions in the lists with information indicators have
limitations or usage notes specific to running the function on a GPU. You can check the usage
notes and limitations in the Extended Capabilities section of the function reference page. For
information about updates to individual gpuArray
enabled functions, see the
release notes.
Toolbox Name  List of Functions with gpuArray Support  GPUSpecific Documentation 

MATLAB  Functions with
gpuArray support  
Statistics and Machine Learning Toolbox™  Functions with
gpuArray support (Statistics and Machine Learning Toolbox)  Analyze and Model Data on GPU (Statistics and Machine Learning Toolbox) 
Image Processing Toolbox™  Functions with
gpuArray support (Image Processing Toolbox)  GPU Computing (Image Processing Toolbox) 
Deep Learning Toolbox™  Functions with
*(see also Deep Learning with GPUs)  Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud (Deep Learning Toolbox) Deep Learning with MATLAB on Multiple GPUs (Deep Learning Toolbox) 
Computer Vision Toolbox™  Functions with
gpuArray support (Computer Vision Toolbox)  GPU Code Generation and Acceleration (Computer Vision Toolbox) 
Communications Toolbox™  Functions with
gpuArray support (Communications Toolbox)  Code Generation and Acceleration Support (Communications Toolbox) 
Signal Processing Toolbox™  Functions with
gpuArray support (Signal Processing Toolbox)  Code Generation and GPU Support (Signal Processing Toolbox) 
Audio Toolbox™  Functions with
gpuArray support (Audio Toolbox)  Code Generation and GPU Support (Audio Toolbox) 
Wavelet Toolbox™  Functions with
gpuArray support (Wavelet Toolbox)  Code Generation and GPU Support (Wavelet Toolbox) 
Curve Fitting Toolbox™  Functions with
gpuArray support (Curve Fitting Toolbox) 
For a list of functions with gpuArray
support in all
MathWorks^{®} products, see gpuArray
supported functions. Alternatively, you can
filter by product. On the Help bar, click Functions.
In the function list, browse the left pane to select a product, for example, MATLAB. At the bottom of the left pane, select GPU Arrays. If you
select a product that does not have gpuArray
enabled functions, then the
GPU Arrays filter is not available.
Deep Learning with GPUs
For many functions in Deep Learning Toolbox, GPU support is automatic if you have a supported GPU and Parallel Computing Toolbox. You do not need to convert your data to gpuArray
. The following is a nonexhaustive list of functions that, by default, run on the GPU if available.
trainNetwork
(Deep Learning Toolbox)predict
(Deep Learning Toolbox)predictAndUpdateState
(Deep Learning Toolbox)classify
(Deep Learning Toolbox)classifyAndUpdateState
(Deep Learning Toolbox)activations
(Deep Learning Toolbox)
For more information about automatic GPU support in Deep Learning Toolbox, see Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud (Deep Learning Toolbox).
For networks and workflows that use networks defined as dlnetwork
(Deep Learning Toolbox) objects or model functions, convert your data to gpuArray
. Use functions with gpuArray
support (Deep Learning Toolbox) to run custom training loops or inference on the GPU.
Check or Select a GPU
If you have a supported GPU, then MATLAB automatically uses it for GPU computation. If you have multiple GPUs, then you can use gpuDeviceTable
to examine the properties of all GPUs detected in your system. You can use gpuDevice
to select one of them, or use multiple GPUs with a parallel pool. For more information, see Identify and Select a GPU Device and Run MATLAB Functions on Multiple GPUs. To check if your GPU is supported, see GPU Computing Requirements.
gpuDeviceTable
Index Name ComputeCapability DeviceAvailable DeviceSelected _____ __________________ _________________ _______________ ______________ 1 "NVIDIA RTX A5000" "8.6" true true 2 "Quadro P620" "6.1" true false
Alternatively, you can determine how many GPU devices are available, inspect some of their properties, and select a device to use from the MATLAB® desktop. On the Home tab, in the Environment area, select Parallel > Select GPU Environment.
Use MATLAB Functions with the GPU
This example shows how to use gpuArray
enabled MATLAB functions to operate with gpuArray
objects. You can check the properties of your GPU using the gpuDevice
function.
gpuDevice
ans = CUDADevice with properties: Name: 'Quadro P620' Index: 2 ComputeCapability: '6.1' SupportsDouble: 1 GraphicsDriverVersion: '511.79' DriverModel: 'WDDM' ToolkitVersion: 11.2000 MaxThreadsPerBlock: 1024 MaxShmemPerBlock: 49152 (49.15 KB) MaxThreadBlockSize: [1024 1024 64] MaxGridSize: [2.1475e+09 65535 65535] SIMDWidth: 32 TotalMemory: 2147287040 (2.15 GB) AvailableMemory: 1615209678 (1.62 GB) CachePolicy: 'balanced' MultiprocessorCount: 4 ClockRateKHz: 1354000 ComputeMode: 'Default' GPUOverlapsTransfers: 1 KernelExecutionTimeout: 1 CanMapHostMemory: 1 DeviceSupported: 1 DeviceAvailable: 1 DeviceSelected: 1
Create a row vector that repeats values from 15 to 15. To transfer it to the GPU and create a gpuArray
object, use the gpuArray
function.
X = [15:15 0 15:15 0 15:15];
gpuX = gpuArray(X);
whos gpuX
Name Size Bytes Class Attributes gpuX 1x95 760 gpuArray
To operate with gpuArray
objects, use any gpuArray
enabled MATLAB function. MATLAB automatically runs calculations on the GPU. For more information, see Run MATLAB Functions on a GPU. For example, use diag
, expm
, mod
, round
, abs
, and fliplr
together.
gpuE = expm(diag(gpuX,1)) * expm(diag(gpuX,1)); gpuM = mod(round(abs(gpuE)),2); gpuF = gpuM + fliplr(gpuM);
Plot the results.
imagesc(gpuF); colormap(flip(gray));
If you need to transfer the data back from the GPU, use gather
. Transferring data back to the CPU can be costly, and is generally not necessary unless you need to use your result with functions that do not support gpuArray
.
result = gather(gpuF);
whos result
Name Size Bytes Class Attributes result 96x96 73728 double
In general, running code on the CPU and the GPU can produce different results due to numerical precision and algorithmic differences between the GPU and CPU. Answers from the CPU and GPU are both equally valid floating point approximations to the true analytical result, having been subjected to different roundoff behavior during computation. In this example, the results are integers and round
eliminates the roundoff errors.
Examples Using GPUs
Examples Running MATLAB Functions on GPUs
The following examples pass gpuArray
objects to supported MATLAB functions, causing those functions to run on the GPU.
Toolbox Name  Examples 

MATLAB  
Image Processing Toolbox 

Deep Learning Toolbox 

Statistics and Machine Learning Toolbox 

Signal Processing Toolbox 

Audio Toolbox  
Communications Toolbox 

Wavelet Toolbox 

Other Examples Using GPUs
The following examples make use of other automatic GPU support.
Toolbox Name  Examples 

Deep Learning Toolbox 

Communications Toolbox 

Acknowledgments
MAGMA is a library of linear algebra routines that take advantage of GPU acceleration. Linear algebra functions implemented for gpuArray
objects in Parallel Computing Toolbox leverage MAGMA to achieve high performance and accuracy.
See Also
gpuArray
 gpuDevice
 gpuDeviceTable
 canUseGPU
Related Examples
 Identify and Select a GPU Device
 Establish Arrays on a GPU
 Measure and Improve GPU Performance
 Sharpen an Image Using the GPU
 Compute the Mandelbrot Set Using GPUEnabled Functions