Hundreds of functions in MATLAB® and other toolboxes run automatically on a GPU if you supply a
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,
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.
gpuArray-enabled functions include the discrete Fourier
fft), matrix multiplication
mtimes), left matrix division (
hundreds of others. For more information, see Check gpuArray-Supported Functions.
If a MATLAB function has support for
gpuArray objects, 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.
For a filtered list of MATLAB functions that support
see Function List
Several MATLAB toolboxes include functions with built-in
gpuArray support. To
view lists of all functions in these toolboxes that support
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
|Toolbox Name||List of Functions with ||GPU-Specific Documentation|
|Statistics and Machine Learning Toolbox™||Functions with
||Analyze and Model Data on GPU (Statistics and Machine Learning Toolbox)|
|Image Processing Toolbox™||Functions with
||GPU Computing (Image Processing Toolbox)|
|Deep Learning Toolbox™|
*(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
||GPU Code Generation and Acceleration (Computer Vision Toolbox)|
|Communications Toolbox™||Functions with
||Code Generation and Acceleration Support (Communications Toolbox)|
|Signal Processing Toolbox™||Functions with
||Code Generation and GPU Support (Signal Processing Toolbox)|
|Audio Toolbox™||Functions with
||Code Generation and GPU Support (Audio Toolbox)|
|Wavelet Toolbox™||Functions with
||Code Generation and GPU Support (Wavelet Toolbox)|
|Curve Fitting Toolbox™||Functions with
You can browse
gpuArray-supported functions from all
MathWorks® products at the following link:
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.
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 non-exhaustive list of
functions that, by default, run on the GPU if available.
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 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
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 and select
your GPU using the
gpuDevice function. 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 an example, see Identify and Select a GPU Device and Use Multiple GPUs in 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
gpuArray-enabled MATLAB functions to operate with
gpuArray objects. You can check the properties of your GPU using the
ans = CUDADevice with properties: Name: 'TITAN RTX' Index: 1 ComputeCapability: '7.5' SupportsDouble: 1 DriverVersion: 11.2000 ToolkitVersion: 11 MaxThreadsPerBlock: 1024 MaxShmemPerBlock: 49152 MaxThreadBlockSize: [1024 1024 64] MaxGridSize: [2.1475e+09 65535 65535] SIMDWidth: 32 TotalMemory: 2.5770e+10 AvailableMemory: 2.4177e+10 MultiprocessorCount: 72 ClockRateKHz: 1770000 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
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
gpuE = expm(diag(gpuX,-1)) * expm(diag(gpuX,1)); gpuM = mod(round(abs(gpuE)),2); gpuF = gpuM + fliplr(gpuM);
Plot the results.
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
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.
This example shows how to sharpen an image using gpuArrays and GPU-enabled functions.
Read the image, and send it to the GPU using the
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
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 GPU-enabled MATLAB functions to compute a well-known mathematical construction: the Mandelbrot set. Check your GPU using the
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 8000 gpuArray y 1x1000 8000 gpuArray
Many MATLAB functions support gpuArrays. When you supply a gpuArray argument to any GPU-enabled 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
[xGrid,yGrid] = meshgrid(x,y); z0 = complex(xGrid,yGrid); count = ones(size(z0),'gpuArray');
The following code implements the Mandelbrot algorithm using GPU-enabled 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
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
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
gpuArray objects 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
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
For example, if creating a
gpuArray that might have negative
G = gpuArray(complex(p)), then you can successfully
Or, within a function passed to
x is a vector of real numbers, and some elements have
sqrt(x) generates an error; instead you should
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
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|
GPU-enabled 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:
gpuArrayobjects contain items such as dimensions, scaling factors, or number of iterations, then the function gathers them and computes on the CPU. Functions only run on the GPU when the actual data arguments are
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.