To use GPU Coder™ for CUDA® C/C++ code generation, install the products specified in Installing Prerequisite Products.
When generating CUDA MEX with GPU Coder, the code generator uses the NVIDIA® compiler and libraries included with MATLAB®. Depending on the operating system on your development computer, you only need to set up the MEX code generator.
If you have multiple versions of Microsoft® Visual Studio® compilers for the C/C++ language installed on your Windows® system, MATLAB selects one as the default compiler. If the selected compiler is not compatible with the version supported by GPU Coder, change the selection. For supported Microsoft Visual Studio versions, see Installing Prerequisite Products.
To change the default compiler, use the
C++ command. When you call
mex -setup C++, MATLAB displays a message with links to set up a different compiler. Select a link
and change the default compiler for building MEX files. The compiler that you choose
remains the default until you call
mex -setup C++ to select a different
default. For more information, see Change Default Compiler. The
mex -setup C++ command changes only the C++ language compiler. You
must also change the default compiler for C by using
MATLAB and the CUDA toolkit support only the GCC/G++ compiler for the C/C++ language on Linux® platforms. For supported GCC/G++ versions, see Installing Prerequisite Products.
Standalone code (static library, dynamically linked library, or executable program) generation has additional set up requirements. GPU Coder uses environment variables to locate the necessary tools, compilers, and libraries required for code generation.
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 does not contain spaces or change Windows settings to enable creation of short names for files, folders, and paths. For more information, see Using Windows short names solution in MATLAB Answers.
Path to the CUDA toolkit installation.
Path to the root folder of cuDNN installation. The root folder contains the bin, include, and lib subfolders.
Path to the root folder of TensorRT installation. The root folder contains the bin, data, include, and lib subfolders.
Path to the build folder of OpenCV on the host. This variable is required for building and running deep learning examples.
Path to the CUDA executables. Generally, the CUDA toolkit installer sets this value automatically.
Path to the
Path to the
Path to the Dynamic-link libraries (DLL) of OpenCV. This variable is required for running deep learning examples.
Path to the CUDA toolkit executable.
Path to the OpenCV libraries. This variable is required for building and running deep learning examples.
Path to the OpenCV header files. This variable is required for building deep learning examples.
Path to the CUDA library folder.
Path to the cuDNN library folder.
Path to the TensorRT™ library folder.
Path to the ARM® Compute Library folder on the target hardware.
Path to the root folder of cuDNN library installation.
Path to the root folder of TensorRT library installation.
Path to the root folder of the ARM Compute Library installation on the ARM target hardware. Set this value on the ARM target hardware.
To verify that your development computer has all the tools and configuration needed for GPU code generation, use the
coder.checkGpuInstall function. This function performs checks to verify if your environment has the all 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 that performs the same checks and can be launched using the command, Check GPU Install.
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 profiling: 0