Establish Arrays on a GPU

A gpuArray in MATLAB^® represents an array that is stored on the GPU. For a complete list of functions that support arrays on the GPU, see Run MATLAB Functions on a GPU.

Create GPU Arrays from Existing Data

Send Arrays to the GPU

GPU arrays can be created by transferring existing arrays from the workspace to the GPU. Use the gpuArray function to transfer an array from MATLAB to the GPU:

N = 6;
M = magic(N);
G = gpuArray(M);

You can accomplish this in a single line of code:

G = gpuArray(magic(N));

G is now a MATLAB gpuArray object that represents the magic square stored on the GPU. The input provided to gpuArray must be numeric (for example: single, double, int8, etc.) or logical. (See also Work with Complex Numbers on a GPU.)

Retrieve Arrays from the GPU

Use the gather function to retrieve arrays from the GPU to the MATLAB workspace. This takes an array that is on the GPU represented by a gpuArray object, and transfers it to the MATLAB workspace as a regular MATLAB array. You can use isequal to verify that you get the correct values back:

G = gpuArray(ones(100,'uint32'));
D = gather(G);
OK = isequal(D,ones(100,'uint32'))

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.

Example: Transfer Array to the GPU

Create a 1000-by-1000 random matrix in MATLAB, and then transfer it to the GPU:

X = rand(1000);
G = gpuArray(X);

Example: Transfer Array of a Specified Precision

Create a matrix of double-precision random values in MATLAB, and then transfer the matrix as single-precision from MATLAB to the GPU:

X = rand(1000);
G = gpuArray(single(X));

Create GPU Arrays Directly

A number of functions allow you to directly construct arrays on the GPU by specifying the 'gpuArray' type as an input argument. These functions require only array size and data class information, so they can construct an array without having to transfer any elements from the MATLAB workspace. For more information, see gpuArray.

Example: Construct an Identity Matrix on the GPU

To create a 1024-by-1024 identity matrix of type int32 on the GPU, type

II = eye(1024,'int32','gpuArray');
size(II)

        1024        1024

With one numerical argument, you create a 2-dimensional matrix.

Example: Construct a Multidimensional Array on the GPU

To create a 3-dimensional array of ones with data class double on the GPU, type

G = ones(100,100,50,'gpuArray');
size(G)

   100   100    50

classUnderlying(G)

double

The default class of the data is double, so you do not have to specify it.

Example: Construct a Vector on the GPU

To create a 8192-element column vector of zeros on the GPU, type

Z = zeros(8192,1,'gpuArray');
size(Z)

        8192           1

For a column vector, the size of the second dimension is 1.

Examine gpuArray Characteristics

There are several functions available for examining the characteristics of a gpuArray object:

Function	Description
`classUnderlying`	Class of the underlying data in the array
`existsOnGPU`	Indication if array exists on the GPU and is accessible
`isreal`	Indication if array data is real
`isaUnderlying`	Determine if tall array data is of specified class, such as `gpuArray`
`isequal`	Determine if two or more arrays are equal
`isnumeric`	Determine if an array is of a numeric data type
`issparse`	Determine if an array is sparse
`length`	Length of vector or largest array dimension
`ndims`	Number of dimensions in the array
`size`	Size of array dimensions