Determine how to modify your MATLAB® code so that the generated code uses less memory. When calling functions, control how data is passed by using the same variables for input and output. Passing by reference reduces the memory used in generated code. Control how memory is allocated by setting limits for stack space usage and by specifying when dynamic memory allocation is used. Minimize code size by disabling features that generate additional code, such as support for integer overflow.
For more information about how to optimize your code for specific conditions, see Optimization Strategies.
Generated Code Optimizations
- Optimization Strategies
Optimize the execution speed or memory usage of generated code.
- Stack Allocation and Performance
Allocate large variables on the heap when you have limited stack space.
- MATLAB Coder Optimizations in Generated Code
To improve the performance of generated code, the code generator uses optimizations.
- Prevent Code Generation for Unused Execution Paths
Make a control-flow variable constant to prevent code generation of unused branches.
- Excluding Unused Paths from Generated Code
Make the control-flow variable constant to prevent generation of code for unused branches.
- Avoid Data Copies of Function Inputs in Generated Code
Generate code that passes input arguments by reference.
- Control Inlining to Fine-Tune Performance and Readability of Generated Code
Inlining eliminates the overhead of function calls but can produce larger C/C++ code and reduce code readability.
- Control Stack Space Usage
Specify the maximum stack space that the generated code can use.
- Fold Function Calls into Constants
Reduce execution time by replacing expression with constant in the generated code.
Numerical Edge Cases
- Disable Support for Integer Overflow or Nonfinites
Improve performance by suppressing generation of supporting code to handle integer overflow or nonfinites.
Custom Code Integration
- Integrate External/Custom Code
Improve performance by integrating your own optimized code.
- Optimize Generated Code for Fast Fourier Transform Functions
Choose the correct fast Fourier transform implementation for your workflow and target hardware.
- Speed Up Linear Algebra in Generated Standalone Code by Using LAPACK Calls
Generate LAPACK calls for certain linear algebra functions. Specify LAPACK library to use.
- Speed Up Matrix Operations in Generated Standalone Code by Using BLAS Calls
Generate BLAS calls for certain low-level matrix operations. Specify BLAS library to use.
- Speed Up Fast Fourier Transforms in Generated Standalone Code by Using FFTW Library Calls
Generate FFTW library calls for fast Fourier transforms. Specify the FFTW library.