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™。您可以在原型设计时加快代码，通信工具箱包括可以在图形处理单元（GPU）上执行的一些功能。

C/C++ Code Generation.开发应用程序后，您可以从Matlab代码和Simulink模型生成便携式C / C ++源代码，独立的可执行文件或独立应用程序。金宝appC / C ++代码生成使您可以在没有MATLAB的计算机上运行模拟，并在您在MATLAB工作时加速处理。有关支持C / C ++代码生成的通信工具箱功能列表，请参阅金宝appFunctions and System Objects Supporting C Code Generation。You needMATLAB Coderto generate C/C++ code. For more information, see从MATLAB代码生成C代码Video。

GPU Code Acceleration.为了在原型设计时加快代码，通信工具箱包含可以在图形处理单元（GPU）上执行的一些功能。你可以使用gpuArray(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

话题

从MATLAB代码生成C代码
Prepare MATLAB code for code generation and generate C-MEX code and a C executable.
Generate C Code from Simulink Model
构建一个可执行文件并在MATLAB中运行可执行文件。
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

GPU Computing tutorial
从MATLAB代码生成C代码Video

Featured Examples

使用MATLAB编码器和并行计算工具箱进行仿真加速度

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.

Open Live Script

End to End System Simulation Acceleration Using GPUs

使用MATLAB®CommunicationGoolbox™软件中的系统对象可用于加速误码率（BER）仿真的四种技术的比较。基于卷积编码的小系统说明了使用MATLAB®Coder™产品，并行循环执行使用的代码生成的效果parforin the Parallel Computing Toolbox™ product, a combination of code generation andparfor和基于GPU的系统对象。

打开脚本

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.

打开脚本

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

使用基于GPU的LDPC解码器系统对象™来提高通信系统仿真的速度。通过建模的ETSI（欧洲电信标准研究所）EN 302 307用于广播，互动服务，新闻收集和其他宽带卫星应用（DVB-S.2）[1]来说明性能改进。有关使用系统对象模拟DVB-S.2系统的更多信息，请参阅DVB-S.2链接，包括LDPC编码。您必须具有并行计算工具箱™用户许可证以使用基于GPU的LDPC解码器。

Open Live Script