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You can profile execution times for MEX functions generated by MATLAB^{®}
Coder™ by using the MATLAB Profiler. The profile for the generated code shows the number of calls and the
time spent for each line of the corresponding MATLAB function. Use the Profiler to identify the lines of MATLAB code that produce generated code that take the most time. This information can
help you identify and correct
performance issues early in the development cycle. For
more information on the MATLAB Profiler, see `profile`

and Profile to Improve Performance (MATLAB).

You can use the MATLAB Profiler with a generated MEX function. Alternatively, if you have a test file that calls your MATLAB function, you can generate the MEX function and profile it in one step. You can perform these operations at the command line or in the MATLAB Coder app.

To use the Profiler with a generated MEX function:

Enable MEX profiling by setting the configuration object property

`EnableMexProfiling`

to`true`

.Alternatively, you can use

`codegen`

with the`-profile`

option.The equivalent setting in the MATLAB Coder app is

**Enable execution profiling**in the**Generate**step.Generate the MEX file

`MyFunction_mex`

.Run the MATLAB Profiler and view the Profile Summary Report, which opens in a separate window.

profile on; MyFunction_mex; profile viewer;

Make sure that you have not changed or moved the original MATLAB file

`MyFunction.m`

. Otherwise, the Profiler does not consider`MyFunction_mex`

for profiling.

If you have a test file `MyFunctionTest.m`

that calls your MATLAB function, you can:

Generate the MEX function and profile it in one step by using

`codegen`

with the`-test`

and the`-profile`

options. If you turned on the MATLAB Profiler before, turn it off before you use these two options together.codegen MyFunction -test MyFunctionTest -profile

Profile the MEX function by selecting

**Enable execution profiling**in the**Verify**step of the app. If you turned on the MATLAB Profiler before, turn it off before you perform this action.

You use the Profiler to identify the functions or the lines of the MATLAB code that produce generated code that take the most time. Following is an
example of a MATLAB function that converts the representation of its input matrices
`A`

and `B`

from row-major to column-major layout in one
of its lines. Such a conversion has a long execution time for large matrices. Avoiding the
conversion by modifying that particular line makes the function more efficient.

Consider the MATLAB function:

function [y] = MyFunction(A,B) %#codegen % Generated code uses row-major representation of matrices A and B coder.rowMajor; length = size(A,1); % Summing absolute values of all elements of A and B by traversing over the % matrices row by row sum_abs = 0; for row = 1:length for col = 1:length sum_abs = sum_abs + abs(A(row,col)) + abs(B(row,col)); end end % Calling external C function 'foo.c' that returns the sum of all elements % of A and B sum = 0; sum = coder.ceval('foo',coder.ref(A),coder.ref(B),length); % Returning the difference of sum_abs and sum y = sum_abs - sum; end

The generated code for this function uses a row-major representation of the square
matrices `A`

and `B`

. The code first computes
`sum_abs`

(the sum of absolute values of
all elements of `A`

and
`B`

) by traversing over the matrices row by row. This algorithm is
optimized for matrices that are represented in a row-major layout. The code then uses
`coder.ceval`

to call the external C function `foo.c`

:

#include <stdio.h> #include <stdlib.h> #include "foo.h" double foo(double *A, double *B, double length) { int i,j,s; double sum = 0; s = (int)length; /*Summing all the elements of A and B*/ for(i=0;i<s*s;i++) { sum += A[i] + B[i]; } return(sum); }

The corresponding C header file `foo.h`

is:

#include "rtwtypes.h" double foo(double *A, double *B, double length);

`foo.c`

returns the variable `sum`

, which is the sum
of all elements of `A`

and
`B`

. The performance of the function
`foo.c`

is independent of whether the matrices `A`

and
`B`

are represented in row-major or column-major layouts.
`MyFunction`

returns the difference of `sum_abs`

and
`sum`

.

You can measure the performance of
`MyFunction`

for large input matrices `A`

and
`B`

, and then optimize it further:

Enable MEX profiling and generate MEX code for

`MyFunction`

. Run`MyFunction_mex`

for two large random matrices`A`

and`B`

. View the Profile Summary Report.A = rand(20000); B = rand(20000); codegen MyFunction -args {A,B} foo.c foo.h -profile profile on; MyFunction_mex(A,B); profile viewer;

A separate window opens showing the Profile Summary Report.

The Profile Summary Report shows the total time and the self time for the MEX file and its child, which is the generated code for the original MATLAB function.

Under Function Name, click the first link to view the Profile Detail Report for the generated code for

`MyFunction`

. You can see the lines where the most time was spent:The line calling

`coder.ceval`

takes the most time (21.152 s). This line has the longest execution time because`coder.ceval`

converts the representation of the matrices`A`

and`B`

from row-major layout to column-major layout before passing them to the external C function. You can avoid this conversion by using an additional argument`-layout:rowMajor`

in`coder.ceval`

:sum = coder.ceval('-layout:rowMajor','foo',coder.ref(A),coder.ref(B),length);

Generate the MEX function and profile again using the modified

`MyFunction`

.The Profile Detail Report forA = rand(20000); B = rand(20000); codegen MyFunction -args {A,B} foo.c foo.h -profile profile on; MyFunction_mex(A,B); profile viewer;

`MyFunction`

shows that the line calling`coder.ceval`

now takes only 0.456 s:

When you use `coder.const`

to fold expressions into constants, it
causes a difference in the code coverage between the MATLAB function and the MEX function. For example, consider the function:

function y = MyFunction %#codegen a = 1; b = 2; c = a + b; y = 5 + coder.const(c); end

Profiling the MATLAB function `MyFunction`

shows this code coverage in the Profile
Detail Report:

However, profiling the MEX function `MyFunction_mex`

shows a different
code coverage:

Lines 2, 3, and 4 are not executed in the generated code because you have folded the
expression `c = a + b`

into a constant for code generation.

This example uses user-defined expression folding. The code generator sometimes automatically folds certain expressions to optimize the performance of the generated code. Such optimizations also cause the coverage of the MEX function to be different from the MATLAB function.

`codegen`

| `coder.MexCodeConfig`

| `coder.ceval`

| `coder.const`

| `coder.rowMajor`

| `profile`