Code Generation and Acceleration Support
You can generate C code from your MATLAB and Simulink model designs. Some Communications Toolbox™ blocks and System objects provide support for C code generation when you use them with MATLAB Coder™. You can speed up your code while prototyping. Communications Toolbox includes some functions that can execute on a Graphics Processing Unit (GPU).
C/C++ Code Generation. After you develop your application, you can generate portable C/C++ source code, standalone executables, or standalone applications from your MATLAB code and Simulink models. C/C++ code generation enables you to run your simulation on machines that do not have MATLAB installed and to speed up processing while you work in MATLAB. For a list of Communications Toolbox features that support C/C++ code generation, see Functions and System Objects Supporting C Code Generation. You need MATLAB Coder to generate C/C++ code. For more information, see Generate C Code from MATLAB Code Video.
GPU Code Acceleration. To speed up your code while prototyping, Communications Toolbox includes some features that can execute on a Graphics Processing Unit (GPU).
You can use the
gpuArray (Parallel Computing Toolbox) function to transfer data to the GPU
and then call the
gather (Parallel Computing Toolbox) function to retrieve the output data
from the GPU. For a list of Communications Toolbox features, see GPU Arrays
Support List for System Objects. You need Parallel Computing Toolbox™ to enable GPU support.
|MATLAB Coder||Generate C code or MEX function from MATLAB code|
- What is C Code Generation from MATLAB?
Introduces code generation support.
- Generate C Code from MATLAB Code
Prepare MATLAB code for code generation and generate C-MEX code and a C executable.
- Generate C Code from Simulink Model
Build an executable and run the executable within MATLAB.
- Generate C Code at the Command Line (MATLAB Coder)
Generate C/C++ code from MATLAB code by using the
- Accelerate Simulation Using GPUs
GPU-based System objects, Guidelines for Using GPUs.
- Run MATLAB Functions on a GPU (Parallel Computing Toolbox)
gpuArrayargument to automatically run functions on a GPU.
- Prerequisites for Deep Learning with MATLAB Coder (MATLAB Coder)
Install products and configure environment for code generation for deep learning networks.
- GPU Computing Requirements (Parallel Computing Toolbox)
Support for NVIDIA® GPU architectures.