Automatically Parallelize for Loops in Generated Code - MATLAB & Simulink (original) (raw)

Iterations of parallel for-loops can run simultaneously on multiple cores on the target hardware. Parallelization of a section of code might significantly improve the execution speed of the generated code. See How parfor-Loops Improve Execution Speed.

While generating C/C++ code from your MATLAB® code, you can generate parallel for-loops automatically. Automatic parallelization is a compiler transformation that converts sequential code to multithreaded code without manual intervention.

Automatic parallelization of for-loop supports these build types for C/C++ targets:

• MEX
• Static library
• Dynamically linked library
• Executable

Parallelize for Loops by Using MATLAB Coder App

To enable automatic parallelization of for-loops, in theMATLAB Coder™ app, in the Generate Code step, select .

Parallelize for Loops at Command Line

You can enable parallelization of the for-loops by using the command-line interface. Consider the functionautoparExample:

function x = autoparExample(x) %#codegen for i = 10:numel(x) x(i) = sqrt(x(i)); end end

To automatically generate parallel for-loops, run these commands:

cfg = coder.config('lib'); cfg.EnableAutoParallelization = 1; x = rand(1,2000); codegen -config cfg autoparExample -args {x} -report

Code generation successful: View report

Inspect Generated Code and Code Insights

Open and inspect the code generation report.

Generated Code

Observe the Open Multiprocessing (OpenMP) pragmas generated above thefor-loops.

void autoparExample(double x[2000]) { int i; if (!isInitialized_autoparExample) { autoparExample_initialize(); } #pragma omp parallel for num_threads(omp_get_max_threads()) private(i)

for (i = 0; i < 1991; i++) { x[i + 9] = sqrt(x[i + 9]); } }

The gutter highlighted in green next to the loops shows the part of the code that is parallelized.

Code Insights

In the Code Insights tab, under Automatic parallelization issues, you can see detailed information about the for-loops that were not parallelized in the generated code.

For example, to view a particular code insight, regenerate code for theautoparExample function that you defined in the previous section. Specify a smaller size for the input arguments.

cfg = coder.config('lib'); cfg.EnableAutoParallelization = 1; x = rand(1,1000); codegen -config cfg autoparExample -args {x} -report

The generated code does not contain parallel for-loops because the size of the input argument is smaller than the threshold value for automatic parallelization. To view detailed information about the nonparallelized part of the code, open the report and click Code Insights > Automatic parallelization issues.

Disable Automatic Parallelization of a for Loop

You might want to disable automatic parallelization of a particular loop if that loop performs better in serial execution. To prevent parallelization of a specificfor-loop, place thecoder.loop.parallelize('never') pragma immediately before the loop in your MATLAB code. This pragma overrides theEnableAutoParallelization setting. Also, this pragma supports only those for loops that are explicitly defined in your MATLAB code. For more information on explicit and implicit loops, see the next section.

For example, the code generator does not parallelize this loop:

% Pragma to disable automatic parallelization of for-loops coder.loop.parallelize('never'); for i = 1:n y(i) = y(i)*sin(i); end

See coder.loop.parallelize.

Parallelize Implicit for Loops

The example function autoparExample used in the previous sections contains an explicit for-loop. But your MATLAB code can also contain implicit for-loops that do not appear explicitly in the code that you author. For example, the MATLAB function mtimes multiplies two matrices and must perform loop iterations implicitly over the matrix elements.

Automatic parallelization supports loops that are implicit in your MATLAB code. For example, consider this functionautoparExample_implicit.

function y = autoparExample_implicit(y) %#codegen y = y * 17; % Generates implicit for loop end

Generate code by running these commands:

cfg = coder.config('lib'); cfg.EnableAutoParallelization = 1; y = rand(1,2000); codegen -config cfg autoparExample_implicit -args {y} -report

Open the report and inspect the generated code. The generated code contains a parallel for-loop for the multiplication operation.

void autoparExample_implicit(double y[2000]) { int i; if (!isInitialized_autoparExample_implicit) { autoparExample_implicit_initialize(); } #pragma omp parallel for num_threads(omp_get_max_threads()) private(i)

for (i = 0; i < 2000; i++) { y[i] *= 17.0; }

Usage Notes and Limitations

• Only outermost loops are parallelized
  Automatic parallelization applies to the outermost loops in your MATLAB code only. This makes inner loops available for vectorization by the system compiler.
• parfor loops remain as parallel loops
  • Automatic parallelization honors parfor loops that you define and does not convert them into sequentialfor-loops.
  • for loops that containparfor-loops are not parallelized.
• Loops containing persistent variables are not parallelized automatically
  Automatic parallelization does not supportfor-loops whose bodies contain either persistent variables or functions that access persistent variables.
• Loops containing external calls are not parallelized automatically
  Automatic parallelization does not supportfor-loops in your code that contain external calls.
• Function not inlined after automatic parallelization
  If a for-loop inside a function is automatically parallelized, the code generator does not inline that function.
• Empty loops and while loops are not parallelized automatically

See Also

parfor | coder.loop.parallelize | coder.config | coder.MexCodeConfig | coder.CodeConfig | coder.EmbeddedCodeConfig

Related Topics

• Reduction Operations Supported for Automatic Parallelization of for-loops
• How parfor-Loops Improve Execution Speed
• Generate SIMD Code from MATLAB Functions for Intel Platforms