Setting Up the Prerequisite Products

To use GPU Coder™ for CUDA^® code generation, set up the prerequisite software. To compile generated code, set up a compiler. To deploy standalone code, you must additionally set environment variables to locate the libraries the generated code uses. For more information about the software and versions required for GPU Coder, see Installing Prerequisite Products.

Set Up Compiler

To compile generated CUDA code, you must install a supported compiler on the host machine.

Windows

For Windows^®, GPU Coder only supports Microsoft^® Visual C++^® compilers. If MATLAB^® detects multiple compilers on your Windows system, it selects one as the default compiler. If the selected compiler is not compatible with GPU Coder, change the selection.

To change the default C++ compiler, use the mex -setup C++ command to display a message with links that you can use to set up a different compiler. The compiler that you choose remains the default until you call mex -setup C++. For more information, see Change Default Compiler. To change the default compiler for C, use mex -setup C.

Linux

On Linux^® platforms, GPU Coder only supports the GCC/G++ compiler for the C/C++ language.

Set Environment Variables

If you want to generate standalone code, you must also set environment variables to locate the tools, compilers, and libraries required for code generation. To generate code for NVIDIA^® GPUs, set up the environment variables to locate the CUDA Toolkit. To use third-party libraries in generated code, you must also set environment variables to locate these libraries.

To generate MEX functions and accelerate Simulink^® simulations on a GPU, GPU Coder uses the host compiler and NVIDIA libraries installed with MATLAB. You do not need to set the environment variables to generate MEX functions or accelerate simulations.

Note

GPU Coder does not support standalone deployment of generated CUDA MEX files using MATLAB Runtime.
In R2025a: The NVIDIA TensorRT^TM library is not installed by default in MATLAB for generating MEX functions or accelerating Simulink simulations. To use TensorRT library, you must install it by using gpucoder.installTensorRT.

Windows

To generate and deploy standalone code on Windows, set these environment variables.

On Windows, a space or special character in the path to the tools, compilers, and libraries can create issues during the build process. You must install third-party software in locations that do not contain spaces or change your Windows settings to enable the creation of short names for files, folders, and paths. For more information, see the Using Windows short names solution in MATLAB Answers.

Variable Name	Description	Example Path
`CUDA_PATH`	Path to the CUDA Toolkit installation.	`C:\Program Files\NVIDIA\CUDA\v12.2\`
`NVIDIA_CUDNN`	Path to the root folder of cuDNN installation. The root folder contains the `bin`, `include`, and `lib` subfolders.	`C:\Program Files\NVIDIA\CUDNN\v8.9\`
`NVIDIA_TENSORRT`	Path to the root folder of TensorRT installation. The root folder contains the `bin`, `data`, `include`, and `lib` subfolders.	`C:\Program Files\NVIDIA\CUDA\v12.2\TensorRT\`
`PATH`	Path to the CUDA executables. The CUDA Toolkit installer typically sets this value automatically.	`C:\Program Files\NVIDIA\CUDA\v12.2\bin`
	Path to the `cudnn.dll` dynamic library. The name of this library may be different on your installation.	`C:\Program Files\NVIDIA\CUDNN\v8.9\bin`
	Path to the `nvinfer*` dynamic libraries of TensorRT. The name of this library may be different on your installation.	`C:\Program Files\NVIDIA\CUDA\v12.2\TensorRT\lib`
	Path to the Dynamic-link libraries (DLL) of OpenCV. This variable is required to run deep learning examples.	`C:\Program Files\opencv\build\x64\vc15\bin`

Linux

To deploy standalone code on Linux, set these environment variables.

Variable	Description	Example Path
`PATH`	Path to the CUDA Toolkit executable.	`/usr/local/cuda/bin`
	Path to the OpenCV libraries. This variable is required for building and running deep learning examples.	`/usr/local/lib/`
	Path to the OpenCV header files. This variable is required for building deep learning examples.	`/usr/local/include/opencv`
`LD_LIBRARY_PATH`	Path to the CUDA library folder.	`/usr/local/cuda/lib64`
	Path to the cuDNN library folder.	`/usr/local/cuda/lib64/`
	Path to the TensorRT™ library folder.	`/usr/local/cuda/TensorRT/lib/`
	Path to the ARM^® Compute Library folder on the target hardware. Set `LD_LIBRARY_PATH` on the ARM target hardware.	`/usr/local/arm_compute/lib/`
`NVIDIA_CUDNN`	Path to the root folder of cuDNN library installation.	`/usr/local/cuda/`
`NVIDIA_TENSORRT`	Path to the root folder of TensorRT library installation.	`/usr/local/cuda/TensorRT/`
`ARM_COMPUTELIB`	Path to the root folder of the ARM Compute Library installation on the ARM target hardware. Set this value on the ARM target hardware.	`/usr/local/arm_compute`

Verify Setup

To verify that your development computer has all the tools and configuration required for GPU code generation, use the coder.checkGpuInstall function. This function performs checks to verify if your environment has all the third-party tools and libraries required for GPU code generation. You must pass a coder.gpuEnvConfig object to the function. This function verifies the GPU code generation environment based on the properties specified in the given configuration object.

You can also use the equivalent GUI-based application, GPU Environment Check that performs the same checks. To open this application, use the MATLAB command, gpucoderSetup.

In the MATLAB Command Window, enter:

gpuEnvObj = coder.gpuEnvConfig;
gpuEnvObj.BasicCodegen = 1;
gpuEnvObj.BasicCodeexec = 1;
gpuEnvObj.DeepLibTarget = 'tensorrt';
gpuEnvObj.DeepCodeexec = 1;
gpuEnvObj.DeepCodegen = 1;
results = coder.checkGpuInstall(gpuEnvObj)

The output shown here is representative. Your results might differ.

Compatible GPU           : PASSED 
CUDA Environment         : PASSED 
	Runtime   : PASSED 
	cuFFT     : PASSED 
	cuSOLVER  : PASSED 
	cuBLAS    : PASSED 
cuDNN Environment        : PASSED 
TensorRT Environment     : PASSED 
Basic Code Generation    : PASSED 
Basic Code Execution     : PASSED 
Deep Learning (TensorRT) Code Generation: PASSED 
Deep Learning (TensorRT) Code Execution: PASSED 

results = 

  struct with fields:

                 gpu: 1
                cuda: 1
               cudnn: 1
            tensorrt: 1
        basiccodegen: 1
       basiccodeexec: 1
         deepcodegen: 1
        deepcodeexec: 1
    tensorrtdatatype: 1