Execution Speed
The code generator increases the execution speed of the generated code where
possible by replacing global variables with local variables, removing data
copies, using the memset and memcpy
functions, and reducing the amount of memory for storing data. You can increase
the execution speed of the generated code by implementing compiler and processor
specific optimizations, specifying buffer reuse, and removing code you might not
need.
Topics
- Optimize Generated Code by Combining Multiple for Constructs
The code generator uses data dependency analysis to combine
forconstructs to reduce static code size and runtime branching. - Configure Loop Unrolling Threshold
Starting at a default value of 5, the code generator begins to use a
forloop instead of separate statements to assign values to the elements of a signal or parameter array. - Eliminate Dead Code Paths in Generated Code
The code generator eliminates dead (that is, unused) code paths from the generated code.
- Floating-Point Multiplication to Handle a Net Slope Correction
For processors that support efficient multiplication, improve code efficiency by using floating-point multiplication to handle a net slope correction.
- Inline Numeric Values of Block Parameters
Reduce global RAM usage by inlining the literal numeric values of block parameters.
- Optimize Generated Code Using Fixed-Point Data with Simulink, Stateflow, and MATLAB
Generate fixed-point code in Simulink®, Stateflow®, and MATLAB®.
- Generate Target Optimizations Within Algorithm Code
Customize generated algorithm code with target-specific optimizations.
- Use Conditional Input Branch Execution
For Switch and Multiport Switch blocks, Simulink executes only blocks that compute the control input and the data input that the control input selects.
- Optimize Generated Code for Complex Signals
The code generator performs various optimizations on the structures that represent signals in the generated code.
- Speed Up Linear Algebra in Code Generated from a MATLAB Function Block
Generate LAPACK calls for certain linear algebra functions in a MATLAB function block. Specify LAPACK library to use.
- Speed Up Matrix Operations in Code Generated from a MATLAB Function Block
Generate BLAS calls for certain low-level matrix operations. Specify BLAS library to use.
- Speed Up Fast Fourier Transforms in Code Generated from a MATLAB Function Block
Generate FFTW library calls for fast Fourier transforms in a MATLAB Function block. Specify the FFTW library.
- Synchronize Multithreaded FFTW Planning in Code Generated from a MATLAB Function Block
Implement FFT library callback class methods and provide supporting C code to prevent concurrent access to FFTW planning.
- Control Memory Allocation for Variable-Size Arrays in a MATLAB Function Block
Disable dynamic memory allocation or specify a dynamic memory allocation threshold for MATLAB Function blocks.
- Generate SIMD Code from Simulink Blocks for Intel Platforms
Improve the execution speed of the generated code using Intel® SSE and Intel AVX technology.
- Optimize Code for Reduction Operations by Using SIMD
Generate optimized code for reduction operations using SIMD instruction sets.
