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Array stored on GPU


A gpuArray object represents an array stored on the GPU. To work with gpuArray objects, use any GPU-enabled MATLAB® function. You can use the array for direct calculations or in CUDA kernels that execute on the GPU. For more information, see Run MATLAB Functions on a GPU.

If you want to retrieve the array from the GPU, for example when using a function that does not support gpuArray objects, use the gather function.


You can load MAT files containing gpuArray data as in-memory arrays when a GPU is not available. A gpuArray loaded without a GPU is limited and you cannot use it for computations. To use a gpuArray loaded without a GPU, retrieve the contents using gather.


Use gpuArray to convert an array in the MATLAB workspace into a gpuArray object. Many MATLAB functions also allow you to create gpuArray objects directly. For more information, see Establish Arrays on a GPU.



G = gpuArray(X) copies the array X to the GPU and returns a gpuArray object.

Input Arguments

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Array to transfer to the GPU, specified as a numeric or logical array. The GPU device must have sufficient free memory to store the data. If X is already a gpuArray object, gpuArray outputs X unchanged.

You can also transfer sparse arrays to the GPU. gpuArray supports only sparse arrays of double-precision.

Example: G = gpuArray(magic(3));

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical
Complex Number Support: Yes

Object Functions

arrayfunApply function to each element of array on GPU
gatherTransfer distributed array or gpuArray to local workspace
pagefunApply function to each page of distributed array or gpuArray

There are several methods for examining the characteristics of a gpuArray object. Most behave like the MATLAB functions of the same name.

existsOnGPUDetermine if gpuArray or CUDAKernel is available on GPU
isequalDetermine array equality
isnumericDetermine whether input is numeric array
issparseDetermine whether input is sparse
isUnderlyingTypeDetermine whether input has specified underlying data type
lengthLength of largest array dimension
ndimsNumber of array dimensions
sizeArray size
underlyingTypeType of underlying data determining array behavior

Other methods for gpuArray objects are too numerous to list here. Most resemble and behave the same as the MATLAB functions of the same name. See Run MATLAB Functions on a GPU.


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This example shows how to use GPU-enabled MATLAB functions to operate with gpuArrays. You can check the properties of your GPU using the gpuDevice function.

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 GPU-enabled 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.


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 use MATLAB functions and operators with gpuArrays to compute the integral of a function, using the Monte Carlo integration method.

Define the number of points to sample. Sample points in the domain of the function, the interval [-1,1] in both x and y coordinates, by creating random points with the rand function. To create a random array directly on the GPU, use the rand function and specify 'gpuArray'. For more information, see Establish Arrays on a GPU.

n = 1e6;
x = 2*rand(n,1,'gpuArray')-1;
y = 2*rand(n,1,'gpuArray')-1;

Define the function to integrate, and use the Monte Carlo integration formula on it. This function approximates the value of π by sampling points within the unit circle. Because the code uses GPU-enabled functions and operators on gpuArrays, the computations automatically run on the GPU. You can perform binary operations such as element-wise multiplication using the same syntax as MATLAB arrays use. To learn more about GPU-enabled functions, see Run MATLAB Functions on a GPU.

f = x.^2 + y.^2 <= 1;
result = 4*1/n*f'*ones(n,1,'gpuArray')
result =


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 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 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 'gpuArray'.

[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;
count = log(count);

When computations are done, plot the results.

colormap([jet();flipud(jet());0 0 0]);
axis off


  • If you need increased performance, or if a function is not available for GPU, gpuArray supports the following options:

    • To precompile and run purely element-wise code on gpuArray objects, use the arrayfun function.

    • To run C++ code containing CUDA® device code or library calls, use a MEX-function. For more information, see Run MEX-Functions Containing CUDA Code.

    • To run existing GPU kernels written in CUDA C++, use the MATLAB CUDAKernel interface. For more information, see Run CUDA or PTX Code on GPU.

    • To generate CUDA code from MATLAB code, use GPU Coder™. For more information, see Get Started with GPU Coder (GPU Coder).

  • You can control the random number stream on the GPU using gpurng.

  • None of the following can exceed intmax('int32'):

    • The number of elements of a dense array.

    • The number of nonzero elements of a sparse array.

    • The size in any given dimension. For example, zeros(0,3e9,'gpuArray') is not allowed.


You can also create a gpuArray object using some MATLAB functions by specifying a gpuArray output. The following table lists the available MATLAB functions that can create gpuArray objects directly.


For class-specific help on the functions with the gpuArray prefix, type

help gpuArray.functionname

where functionname is the name of the method. For example, to get help on colon, type

help gpuArray.colon
Introduced in R2010b