Main Content

Execution Speed

Improve execution speed of generated C/C++ code

Use code generation options and optimizations to improve the execution speed of the generated code. You can modify or disable dynamic memory allocation, which can affect execution speed. Parallelized code can be generated by using parfor loops. When available, take advantage of preexisting optimized C code and specialized libraries to speed up execution.

For more information about how to optimize your code for specific conditions, see Optimization Strategies.


parforParallel for-loop
coder.varsizeDeclare variable-size data
coder.constFold expressions into constants in generated code
coder.inlineControl inlining of a specific function in generated code
coder.loop.parallelizeDisable automatic parallelization of a for loop
coder.unrollUnroll for-loop by making a copy of the loop body for each loop iteration
coder.cevalCall external C/C++ function


coder.LAPACKCallbackAbstract class for specifying the LAPACK library and LAPACKE header file for LAPACK calls in generated code
coder.BLASCallbackAbstract class for specifying the BLAS library and CBLAS header and data type information for BLAS calls in generated code
coder.fftw.StandaloneFFTW3Interface Abstract class for specifying an FFTW library for FFTW calls in generated code

Examples and How To

Variable-Size Arrays

Minimize Dynamic Memory Allocation

Improve execution time by minimizing dynamic memory allocation.

Provide Maximum Size for Variable-Size Arrays

Use techniques to help the code generator determine the upper bound for a variable-size array.

Disable Dynamic Memory Allocation During Code Generation

Disable dynamic memory allocation in the app or at the command line.

Set Dynamic Memory Allocation Threshold

Disable dynamic memory allocation for arrays less than a certain size.


Generate Code with Parallel for-Loops (parfor)

Generate a loop that runs in parallel on shared-memory multicore platforms.

Specify Maximum Number of Threads in parfor-Loops

Generate a MEX function that executes loop iterations in parallel on specific number of available cores.

Control Compilation of parfor-Loops

Treat parfor-loops as parfor-loops that run on a single thread.

Install OpenMP Library on macOS Platform

Install OpenMP library to generate parallel for-loops on macOS platform.

Minimize Redundant Operations in Loops

Move operations outside of loop when possible.

Unroll for-Loops

Control loop unrolling.

Automatically Parallelize for Loops in Generated Code

Iterations of parallel for loops can run simultaneously on multiple cores on the target hardware.

Function Calls

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.

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.

External Code Integration

Integrate External/Custom Code

Improve performance by integrating your own optimized code.

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.

Synchronize Multithreaded Access to FFTW Planning in Generated Standalone Code

Implement FFT library callback class methods and provide supporting C code to prevent concurrent access to FFTW planning.


Optimization Strategies

Optimize the execution speed or memory usage of generated code.

Dynamic Memory Allocation and Performance

Dynamic memory allocation can slow down execution speeds.

Algorithm Acceleration Using Parallel for-Loops (parfor)

Generate MEX functions for parfor-loops.

Classification of Variables in parfor-Loops

Variables inside parfor-loops are classified as loop, sliced, broadcast, reduction, or temporary.

Reduction Assignments in parfor-Loops

A reduction variable accumulates a value that depends on all the loop iterations together.

MATLAB Coder Optimizations in Generated Code

To improve the performance of generated code, the code generator uses optimizations.

memcpy Optimization

The code generator optimizes generated code by using memcpy.

memset Optimization

The code generator optimizes generated code by using memset.

LAPACK Calls in Generated Code

LAPACK function calls improve the execution speed of code generated for certain linear algebra functions.

BLAS Calls in Generated Code

BLAS function calls improve the execution speed of code generated for certain low-level vector and matrix operations.

Generate Code That Uses Row-Major Array Layout

Generate C/C++ code with row elements stored contiguously in memory.


Troubleshooting parfor-Loops

Diagnose errors for code generation of parfor-loops.

MEX Generated on macOS Platform Stays Loaded in Memory

Troubleshoot issues that occur when the source MATLAB® code contains global or persistent variables that are reachable from the body of a parfor-loop.

Resolve Issue: Array or Variable Access Pattern Not Suitable for Parallel Execution

Troubleshoot automatic parallelization failure caused by memory access pattern inside a for loop.

Featured Examples