single

Single-precision arrays

Description

Single-precision variables in MATLAB^® are stored as 4-byte (32-bit) floating-point values of data type (class) single. For example:

y = single(10);
whos y

Name      Size            Bytes  Class     Attributes

  y         1x1                 4  single              

For more information on floating-point values, see Floating-Point Numbers.

Creation

If you have an array of a different type, such as double or int8, then you can convert that array to a single precision array by using the single function.

Syntax

Y = single(X)

Description

Y = single(X) converts the values in X to single precision.

Input Arguments

X — Input array

scalar | vector | matrix | multidimensional array

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

Data Types: double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char

Examples

Convert to Single-Precision Variable

Convert a double-precision variable to single precision with the single function.

x = 100;
xtype = class(x)

xtype = 
'double'

y = single(x)

y = single

100

Extended Capabilities

Tall Arrays

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

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

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• When using single-precision integers with the colon operator, if either of the end points have a value that is greater in absolute value than flintmax('single') in your MATLAB code, then the generated code might produce different values as compared to the MATLAB code. For example:

  function z = mismatch_values
  a = single(1);
  b = flintmax('single') + 2;
  d = single(9);
  z = a:d:b;
  end

  The z values that are calculated by the generated code and MATLAB code are different due to the different rounding methods used by the generated code and MATLAB code.

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 single 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