uint32

32-bit unsigned integer arrays

Description

Variables in MATLAB^® of data type (class) uint32 are stored as 4-byte (32-bit) unsigned integers. For example:

y = uint32(10);
whos y

  Name      Size            Bytes  Class     Attributes

  y         1x1                 4  uint32                 

For more information on integer types, see Integers.

Creation

Some array creation functions allow you to specify the data type. For instance, zeros(100,'uint32') creates a 100-by-100 matrix of zeros of type uint32.

If you have an array of a different type, such as double or single, then you can convert that array to an array of type uint32 by using the uint32 function.

Syntax

Y = uint32(X)

Description

Y = uint32(X) converts the values in X to type uint32. Values outside the range [0,2³²-1] map to the nearest endpoint.

Input Arguments

X — Input array

scalar | vector | matrix | multidimensional array

Input array, specified as a scalar, vector, matrix, or multidimensional array.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint64 | logical | char

Examples

Convert to 32-Bit Unsigned Integer Variable

Convert a double-precision variable to a 32-bit unsigned integer.

x = 100;
xtype = class(x)

xtype = 
'double'

y = uint32(x)

y = uint32

100

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

The uint32 function fully supports tall arrays. For more information, see Tall Arrays.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The uint32 function fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray (Parallel Computing Toolbox). For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a