Setting Up the Prerequisite Products

Environment Variables

GPU Coder™ uses environment variables to locate the necessary tools, compilers, and libraries required for code generation. If you have a non-standard installation of the required third-party products, ensure that the following environment variables are set.

Note

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.

Platform	Variable Name	Description
Windows	CUDA_PATH	Path to the CUDA® toolkit installation. For example: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\
	NVIDIA_CUDNN	Path to the root folder of cuDNN installation. The root folder contains the bin, include, and lib subfolders. For example: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\cuDNN\
	OPENCV_DIR	Path to the build folder of OpenCV. This variable is required for building deep learning examples. For example: C:\Program Files\opencv\build
	PATH	Path to the CUDA executables. Generally, the CUDA toolkit installer sets this value automatically. For example: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin
		Path to the cudnn.dll dynamic library. The name of this library may be different on your installation. For example: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\cuDNN\bin
		Path to the Dynamic-link libraries (DLL) of OpenCV. This variable is required for running deep learning examples. For example: C:\Program Files\opencv\build\x64\vc15\bin
Linux®	PATH	Path to the CUDA toolkit executable. For example: /usr/local/cuda-10.0/bin
		Path to the OpenCV libraries. This variable is required for building and running deep learning examples. For example: /usr/local/lib/
		Path to the OpenCV header files. This variable is required for building deep learning examples. For example: /usr/local/include/opencv
	LD_LIBRARY_PATH	Path to the CUDA library folder. For example: /usr/local/cuda-10.0/lib64
		Path to the cuDNN library folder. For example: /usr/local/cuDNN/lib64/
		Path to the TensorRT™ library folder. For example: /usr/local/TensorRT/lib/
	NVIDIA_CUDNN	Path to the root folder of cuDNN library installation. For example: /usr/local/cuDNN/
	NVIDIA_TENSORRT	Path to the root folder of TensorRT library installation. For example: /usr/local/TensorRT/
	LINARO_TOOLCHAIN_4_9 or LINARO_TOOLCHAIN_4_8	Path to the root folder of Linaro installation. For example: /usr/local/gcc-linaro
	NVIDIA_CUDA_TX2 or NVIDIA_CUDA_TX1 or NVIDIA_CUDA_TK1	Path to the root folder of CUDA toolkit for ARM®. This variable is required for targeting NVIDIA® GPU boards. For example: /usr/local/cuda-6.5-armhf/

MEX Setup

Windows Systems

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 mex -setup command. When you call mex -setup, 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 to select a different default. For more information, see Change Default Compiler (MATLAB). The mex -setup command changes only the C language compiler. You must also change the default compiler for C++ by using mex -setup C++.

Linux Platform

MATLAB and the CUDA toolkit support only the GCC compiler for the C language on Linux platforms. For supported GCC versions, see Installing Prerequisite Products.

Verify Setup

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, coder.checkGpuInstallApp.

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