Hundreds of functions in MATLAB^{®} and other toolboxes run automatically on a GPU if you supply a
gpuArray
argument.
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 MATLAB data is more appropriate (for example, size
). You
can mix inputs using both gpuArray and MATLAB arrays in the same function call. To learn more about when a function
runs on GPU or CPU, see Special Conditions for gpuArray Inputs. GPUenabled
functions include the discrete Fourier transform (fft
), matrix
multiplication (mtimes
), left matrix division
(mldivide
), and hundreds of others. For more information, see
Check GPUSupported Functions.
If a MATLAB function has support for gpuArrays, you can consult additional GPU usage information on its function page. See GPU Arrays in the Extended Capabilities section at the end of the function page.
Tip
For a filtered list of MATLAB functions that support GPU arrays, see Function List (GPUarrays).
Several MATLAB toolboxes include functions with builtin GPU support. To view lists of all functions in these toolboxes that support gpuArrays, use the links in the following table. Functions in the lists with warning 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 GPUenabled 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)  
Image Processing Toolbox™  Functions with gpuArray support (Image Processing Toolbox)  GPU Computing (Image Processing Toolbox) 
Deep Learning Toolbox™ 
Functions with gpuArray support (Deep Learning Toolbox) *(see also Deep Learning with GPUs) 

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) 
You can browse GPUsupported functions from all MathWorks^{®} products at the following link: GPUsupported 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 GPUenabled functions, then the GPU Arrays filter is not available.
For many functions in Deep Learning Toolbox, GPU support is automatic if you have a suitable 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 GPUsupport in Deep Learning Toolbox, see Deep Learning with Big Data on GPUs and in Parallel (Deep Learning Toolbox).
For advanced 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 prediction on the GPU.
If you have a GPU, then MATLAB automatically uses it for GPU computations. You can check your GPU
using the gpuDevice
function. If you have
multiple GPUs, then you can use gpuDevice
to select one of them,
or use multiple GPUs with a parallel pool. For an example, see Identify and Select a GPU and Use Multiple GPUs in a Parallel Pool. To check if
your GPU is supported, see GPU Support by Release.
For deep learning, MATLAB provides automatic parallel support for multiple GPUs. See Deep Learning with MATLAB on Multiple GPUs (Deep Learning Toolbox).
This example shows how to use GPUenabled MATLAB functions to operate with gpuArrays. You can check the properties of your GPU using the gpuDevice
function.
gpuDevice
ans = CUDADevice with properties: Name: 'GeForce GTX 1080' Index: 1 ComputeCapability: '6.1' SupportsDouble: 1 DriverVersion: 10.1000 ToolkitVersion: 10.1000 MaxThreadsPerBlock: 1024 MaxShmemPerBlock: 49152 MaxThreadBlockSize: [1024 1024 64] MaxGridSize: [2.1475e+09 65535 65535] SIMDWidth: 32 TotalMemory: 8.5899e+09 AvailableMemory: 6.9012e+09 MultiprocessorCount: 20 ClockRateKHz: 1733500 ComputeMode: 'Default' GPUOverlapsTransfers: 1 KernelExecutionTimeout: 1 CanMapHostMemory: 1 DeviceSupported: 1 DeviceSelected: 1
Create a row vector that repeats values from 15 to 15. To transfer it to the GPU and create a gpuArray, 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 4 gpuArray
To operate with gpuArrays, use any GPUenabled MATLAB function. MATLAB automatically runs calculations on the GPU. For more information, see Run MATLAB Functions on a GPU. For example, use a combination of diag
, expm
, mod
, round
, abs
, and fliplr
.
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
. Gathering 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 there can be differences in the results if you run the code on the CPU, due to numerical precision and algorithmic differences between GPU and CPU. Answers on CPU and GPU are both equally valid floating point approximations to the true analytical result, having been subjected to different roundoff during computation. In this example, the results are integers and round
eliminates the roundoff errors.
This example shows how to sharpen an image using gpuArrays and GPUenabled functions.
Read the image, and send it to the GPU using the gpuArray
function.
image = gpuArray(imread('peppers.png'));
Convert the image to doubles, and apply convolutions to obtain the gradient image. Then, using the gradient image, sharpen the image by a factor of amount
.
dimage = im2double(image); gradient = convn(dimage,ones(3)./9,'same')  convn(dimage,ones(5)./25,'same'); amount = 5; sharpened = dimage + amount.*gradient;
Resize, plot and compare the original and sharpened images.
imshow(imresize([dimage, sharpened],0.7));
title('Original image (left) vs sharpened image (right)');
This example shows how to use GPUenabled MATLAB functions to compute a wellknown mathematical construction: the Mandelbrot set. Check your GPU using the gpuDevice
function.
Define the parameters. The Mandelbrot algorithm iterates over a grid of real and imaginary parts. The following code defines the number of iterations, grid size, and grid limits.
maxIterations = 500; gridSize = 1000; xlim = [0.748766713922161, 0.748766707771757]; ylim = [ 0.123640844894862, 0.123640851045266];
You can use the gpuArray
function to transfer data to the GPU and create a gpuArray
, or you can create an array directly on the GPU. gpuArray
provides GPU versions of many functions that you can use to create data arrays, such as linspace
. For more information, see Create GPU Arrays Directly.
x = gpuArray.linspace(xlim(1),xlim(2),gridSize); y = gpuArray.linspace(ylim(1),ylim(2),gridSize); whos x y
Name Size Bytes Class Attributes x 1x1000 4 gpuArray y 1x1000 4 gpuArray
Many MATLAB functions support gpuArrays. When you supply a gpuArray argument to any GPUenabled function, the function runs automatically on the GPU. For more information, see Run MATLAB Functions on a GPU. Create a complex grid for the algorithm, and create the array count
for the results. To create this array directly on the GPU, use the ones
function, and specify 'gpuArray'
.
[xGrid,yGrid] = meshgrid(x,y);
z0 = complex(xGrid,yGrid);
count = ones(size(z0),'gpuArray');
The following code implements the Mandelbrot algorithm using GPUenabled functions. Because the code uses gpuArrays, the calculations happen on the GPU.
z = z0; for n = 0:maxIterations z = z.*z + z0; inside = abs(z) <= 2; count = count + inside; end count = log(count);
When computations are done, plot the results.
imagesc(x,y,count)
colormap([jet();flipud(jet());0 0 0]);
axis off
The following functions support sparse gpuArrays.
abs acos acosd acosh acot acotd acoth acsc acscd acsch angle asec asecd asech asin asind asinh atan atand atanh bicg bicgstab ceil cgs classUnderlying conj cos cosd cosh cospi cot cotd coth csc cscd csch ctranspose deg2rad diag 
end eps exp expint expm1 find fix floor full gmres gpuArray.speye imag isaUnderlying isdiag isempty isequal isequaln isfinite isfloat isinteger islogical isnumeric isreal issparse istril istriu isUnderlyingType length log log2 log10 log1p lsqr minus mtimes mustBeUnderlyingType ndims nextpow2 nnz 
nonzeros norm numel nzmax pcg plus qmr rad2deg real reallog realsqrt round sec secd sech sign sin sind sinh sinpi size sparse spfun spones sprandsym sqrt sum tan tand tanh tfqmr times (.*) trace transpose tril triu uminus underlyingType uplus 
You can create a sparse gpuArray either by calling sparse
with a gpuArray input, or by calling gpuArray
with a sparse input. For
example,
x = [0 1 0 0 0; 0 0 0 0 1]
0 1 0 0 0 0 0 0 0 1
s = sparse(x)
(1,2) 1 (2,5) 1
g = gpuArray(s); % g is a sparse gpuArray gt = transpose(g); % gt is a sparse gpuArray f = full(gt) % f is a full gpuArray
0 0 1 0 0 0 0 0 0 1
Sparse gpuArrays do not support indexing. Instead, use find
to locate nonzero elements of the array and their row and
column indices. Then, replace the values you want and construct a new sparse
gpuArray.
If the output of a function running on the GPU could potentially be complex, you
must explicitly specify its input arguments as complex. This applies to
gpuArray
or to functions called in code run by
arrayfun
.
For example, if creating a gpuArray that might have negative elements, use
G = gpuArray(complex(p))
, then you can successfully execute
sqrt(G)
.
Or, within a function passed to arrayfun
, if
x
is a vector of real numbers, and some elements have
negative values, sqrt(x)
generates an error; instead you should
call sqrt(complex(x))
.
If the result is a gpuArray of complex data and all the imaginary parts are zero,
these parts are retained and the data remains complex. This could have an impact
when using sort
, isreal
, and so on.
The following table lists the functions that might return complex data, along with the input range over which the output remains real.
Function  Input Range for Real Output 

acos(x)  abs(x) <= 1 
acosh(x)  x >= 1 
acoth(x)  abs(x) >= 1 
acsc(x)  abs(x) >= 1 
asec(x)  abs(x) >= 1 
asech(x)  0 <= x <= 1 
asin(x)  abs(x) <= 1 
atanh(x)  abs(x) <= 1 
log(x)  x >= 0 
log1p(x)  x >= 1 
log10(x)  x >= 0 
log2(x)  x >= 0 
power(x,y)  x >= 0 
reallog(x)  x >= 0 
realsqrt(x)  x >= 0 
sqrt(x)  x >= 0 
GPUenabled functions run on the GPU only when the data is on the GPU. For example, the following code runs on GPU because the data, the first input, is on the GPU:
>> sum(gpuArray(magic(10)),2);
>> sum(magic(10),gpuArray(2));
MAGMA is a library of linear algebra routines that take advantage of GPU acceleration. Linear algebra functions implemented for gpuArrays in Parallel Computing Toolbox leverage MAGMA to achieve high performance and accuracy.