Code Generation and Acceleration Support

Generate C code from MATLAB^®code and Simulink^®, and use GPUs to accelerate processing

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 withMATLAB 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.在开发应用程序之后,您可以属te 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, seeFunctions and System Objects Supporting C Code Generation. You needMATLAB Coderto generate C/C++ code. For more information, seeGenerate 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 thegpuArray(Parallel Computing Toolbox)function to transfer data to the GPU and then call thegather(Parallel Computing Toolbox)function to retrieve the output data from the GPU. For a list of Communications Toolbox features that support GPU code generation, seeGPU Arrays Support List for System Objects. You need Parallel Computing Toolbox™ to enable GPU support.

Apps

MATLAB Coder

Generate C code or MEX function fromMATLABcode

Functions

`codegen`	Generate C/C++ code fromMATLABcode
`gather`	Transfer distributed array or gpuArray to local workspace
`gpuArray`	Array stored on GPU

Topics

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 thecodegencommand.
Run MATLAB Functions on a GPU(Parallel Computing Toolbox)
Hundreds of functions in MATLAB and other toolboxes run automatically on a GPU if you supply agpuArray(Parallel Computing Toolbox) argument.
Prerequisites for Deep Learning with MATLAB Coder(MATLAB Coder)
Install products and configure environment for code generation for deep learning networks.
GPU Support by Release(Parallel Computing Toolbox)
Support for NVIDIA^®GPU architectures.

Related Information

Featured Examples

Simulation Acceleration Using MATLAB Coder and Parallel Computing Toolbox

Two ways to accelerate the simulation of communications algorithms in MATLAB®. It showcases the runtime performance effects of using MATLAB to C code generation and parallel processing runs (using the MATLAB parfor function). For a comprehensive look at all possible acceleration techniques, see Accelerating MATLAB Algorithms and Applications article.

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End to End System Simulation Acceleration Using GPUs

A comparison of four techniques which can be used to accelerate bit error rate (BER) simulations using System objects in the MATLAB® Communications Toolbox™ software. A small system, based on convolutional coding, illustrates the effect of code generation using the MATLAB® Coder™ product, parallel loop execution usingparforin the Parallel Computing Toolbox™ product, a combination of code generation andparfor, and GPU-based System objects.

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使用gpu加速Ra Turbo编码比特错误te Simulations

Use GPUs to dramatically accelerate bit error rate simulations. Turbo Codes form the backbone of many modern communication systems. Because of the intense amount of computation involved in a Turbo Decoder and the massive amount of trials required for a valid bit error rate simulation, the Turbo Decoder is an ideal candidate for GPU acceleration. See the Parallel Concatenated Convolutional Coding: Turbo Codes example, which explains the data processing chain, for more information on Turbo Codes.

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DVB-S.2 System Simulation Using a GPU-Based LDPC Decoder System Object

Use a GPU-based LDPC Decoder System object™ to increase the speed of a communications system simulation. The performance improvement is illustrated by modeling part of the ETSI (European Telecommunications Standards Institute) EN 302 307 standard for Broadcasting, Interactive Services, News Gathering and other broadband satellite applications (DVB-S.2) [ 1 ]. For further information on using System objects to simulate the DVB-S.2 system see DVB-S.2 Link, Including LDPC Coding. You must have a Parallel Computing Toolbox™ user license to use the GPU-based LDPC Decoder.

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