coder.loop.parallelize

Parallelize specific for loops in generated code; disable automatic parallelization

Since R2021a

Syntax

coder.loop.parallelize('never')

coder.loop.parallelize("loopID")

loopObj = coder.loop.parallelize(___)

Description

coder.loop.parallelize('never') disables automatic parallelization of the for loop placed immediately after it. This pragma overrides the EnableAutoParallelization configuration setting.

This pragma supports explicit for loops only. For more information on explicit and implicit loops, see Parallelize Implicit for Loops.

coder.loop.parallelize("loopID") parallelizes the for-loop whose index name is loopID.

This prompts the generated code for that loop to execute the iterations in parallel with the threads available for your target. This transforms requires EnableOpenMP to be set to true in your code configuration object.

For more information about loop optimizations, see Optimize Loops in Generated Code.

loopObj = coder.loop.parallelize(___) creates a loop control object with transformSchedule property set to coder.loop.parallelize. Use the apply method to apply the transform to the specified loop.

Examples

Disable Automatic Parallelization of a Specific for loop

This example shows how to disable the automatic parallelization of a specific for loop in your MATLAB^® code by using the coder.loop.parallelize('never') directive.

Define the MATLAB function autoparExample:

function [x, y] = autoparExample(x, y) %#codegen
x = x * 17;
% Pragma to disable automatic parallelization of for-loops
coder.loop.parallelize('never');
for i = 10:numel(x)
    y(i) = sqrt(y(i));
end
end

This function contains two loops:

• The mtimes (matrix multiplication) function contains an implicit for loop that iterates over the elements of the matrix x.

• The explicit for loop that calculates the square root of a subset of the elements of the matrix y. The coder.loop.parallelize('never') directive disables automatic parallelization of this loop.

Create a code generation configuration object for a static library. Set the EnableAutoParallelization property to true. Specify the input types as 1-by-2000 row vectors of doubles and generate code.

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

Code generation successful: View report

Open the code generation report and inspect the generated autoparExample function:

• The generated code contains a parallel loop for the multiplication operation.

• The explicit for loop is converted to a serial loop in the generated code. This loop was preceded by the coder.loop.parallelize('never') directive in your MATLAB code. The code generator does not parallelize it.

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

  for (i = 0; i < 2000; i++) {
    x[i] *= 17.0;
  }
  /*  Pragma to disable automatic parallelization of for-loops */
  for (b_i = 0; b_i < 1991; b_i++) {
    y[b_i + 9] = sqrt(y[b_i + 9]);
  }
}

Selectively Parallelize for Loops in Generated Code

You can choose to parallelize specific for-loops in your generated code by using the coder.loop.parallelize function in your MATLAB code.

Define a MATLAB function forLoopFuncParallel. Within the function, call coder.loop.parallelize with the for loop index to be parallelize. Also call the function to prevent parallelization of a specific loop.

function [out1,out2] = forLoopFuncParallel
out1 = ones(1,100);
out2 = zeros(1,100);

coder.loop.parallelize("i");
for i = 1:100
    out1(i) = out1(i) * i;
end

coder.loop.parallelize("j");
for j = 1:100
    out2(j) = out2(j) + j;
end

Generate code for this function by running the following command:

codegen forLoopFuncParallel -config:lib -launchreport

Inspect the generated code in the code generation report. Notice that the code generator uses OpenMP to parallelize the loop.

void forLoopFuncParallel(double out1[100], double out2[100])
{
  int i;
  int j;
  if (!isInitialized_forLoopFuncParallel) {
    forLoopFuncParallel_initialize();
  }
  for (j = 0; j < 100; j++) {
    out1[j] = 1.0;
    out2[j] = 0.0;
  }
#pragma omp parallel for num_threads(omp_get_max_threads())

  for (i = 0; i < 100; i++) {
    out1[i] *= (double)i + 1.0;
  }
  for (j = 0; j < 100; j++) {
    out2[j] += (double)j + 1.0;
  }
}

Input Arguments

loopID — for-loop index name

string scalar | character vector

for loop identifier or index name to parallelize, specified as a character vector a string scalar.

Data Types: char | string

Output Arguments

loopObj — Loop control object

coder.loop.Control object

Loop control object with transformSchedule property set to coder.loop.parallelize.

Extended Capabilities

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

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

Version History

Introduced in R2021a