MEX Profile Generation

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:

If you have a test fileMyFunctionTest.mthat calls your MATLAB function, you can:

Example

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 matricesAandBfrom 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 Bcoder.rowMajor; length = size(A,1);% Summing absolute values of all elements of A and B by traversing over the% matrices row by rowsum_abs = 0;forrow = 1:lengthforcol = 1:length sum_abs = sum_abs + abs(A(row,col)) + abs(B(row,col));endend% Calling external C function 'foo.c' that returns the sum of all elements% of A and Bsum = 0; sum = coder.ceval('foo',coder.ref(A),coder.ref(B),length);% Returning the difference of sum_abs and sumy = sum_abs - sum;end

The generated code for this function uses a row-major representation of the square matricesAandB. The code first computessum_abs(the sum of absolute values of all elements ofAandB) 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 usescoder.cevalto call the external C functionfoo.c:

#include  #include  #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
            

The corresponding C header filefoo.his:

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

foo.creturns the variablesum, which is the sum of all elements ofAandB. The performance of the functionfoo.cis independent of whether the matricesAandBare represented in row-major or column-major layouts.MyFunctionreturns the difference ofsum_absandsum.

You can measure the performance ofMyFunctionfor large input matricesAandB, and then optimize it further:

Effect of Folding Expressions on MEX Code Coverage

When you usecoder.constto 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:

functiony = MyFoldFunction%#codegena = 1; b = 2; c = a + b; y = 5 + coder.const(c);end

Profiling the MATLAB functionMyFoldFunctionshows this code coverage in the Profile Detail Report:

However, profiling the MEX functionMyFoldFunction_mexshows a different code coverage:

Lines 2, 3, and 4 are not executed in the generated code because you have folded the expressionc = a + binto 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.