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 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.

Apps

MATLAB Coder

Generate C code or MEX function from MATLAB code

Functions

`codegen`	Generate C/C++ code from MATLAB code
`gather`	Transfer distributed array, `Composite` object, or `gpuArray` object to local workspace
`gpuArray`	Array stored on GPU

Topics

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)
Use command line tools to generate C code from an example MATLAB function.
Accelerate Simulation Using GPUs
GPU-based System objects, Guidelines for Using GPUs.
Run MATLAB Functions on a GPU (Parallel Computing Toolbox)
Supply a gpuArray argument 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.

Related Information

Featured Examples

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Accelerate MIMO-OFDM Link Simulation Using GPU

Accelerate a MIMO-OFDM link simulation with LDPC channel coding by using a GPU.

Since R2024a

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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.

Open Live Script

Accelerate SISO Single-Carrier Link Simulation Using GPU

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 using parfor in the Parallel Computing Toolbox™ product, a combination of code generation and parfor, and GPU-based System objects.

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Using GPUs to Accelerate Turbo Coding Bit Error Rate 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 large number 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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LDPC Link Simulation Using GPU Processing

Use the ldpcDecode function and gpuArray 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 (DVBS-2) [ 1 ]. For further information on simulating the DVBS-2 system see DVBS-2 Link, Including LDPC Coding. You must have a Parallel Computing Toolbox™ user license to use GPU processing for ldpcDecode.

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5G LDPC Block Error Rate Simulation Using the Cloud or a Cluster

Use the cloud or a cluster for block error rate (BLER) simulation of low-density parity-check (LDPC) coding for the 5G NR downlink shared transport channel (DL-SCH).

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