Before installing the CUDA Toolkit, you should read the Release Notes, as they provide important details on installation and software functionality.

This document is intended for readers familiar with the Mac OS X environment and the compilation of C programs from the command line. You do not need previous experience with CUDA or experience with parallel computation.

To verify that your system is CUDA-capable, under the Apple menu select About This Mac, click the More Info … button, and then select Graphics/Displays under the Hardware list. There you will find the vendor name and model of your graphics card. If it is an NVIDIA card that is listed on the CUDA-supported GPUs page, your GPU is CUDA-capable.

The Release Notes for the CUDA Toolkit also contain a list of supported products.

The CUDA Development Tools require an Intel-based Mac running Mac OSX v. 10.12. To check which version you have, go to the Apple menu on the desktop and select About This Mac.

A supported version of Xcode must be installed on your system. The list of supported Xcode versions can be found in the System Requirements section. The latest version of Xcode can be installed from the Mac App Store.

Older versions of Xcode can be downloaded from the Apple Developer Download Page. Once downloaded, the Xcode.app folder should be copied to a version-specific folder within /Applications. For example, Xcode 6.2 could be copied to /Applications/Xcode_6.2.app.

sudo xcode-select -s /Applications/<Xcode_install_dir>/Contents/Developer

The CUDA Toolkit requires that the native command-line tools are already installed on the system. Xcode must be installed before these command-line tools can be installed.

$ xcode-select --install

Note: It is recommended to re-run the above command if Xcode is upgraded, or an older version of Xcode is selected.

$ /usr/bin/cc --version

Once you have verified that you have a supported NVIDIA GPU, a supported version the MAC OS, and clang, you need to download the NVIDIA CUDA Toolkit.

The download can be verified by comparing the posted MD5 checksum with that of the downloaded file. If either of the checksums differ, the downloaded file is corrupt and needs to be downloaded again.

$ openssl md5 <file>

Use the following procedure to successfully install the CUDA driver and the CUDA toolkit. The CUDA driver and the CUDA toolkit must be installed for CUDA to function. If you have not installed a stand-alone driver, install the driver provided with the CUDA Toolkit.

export PATH=/Developer/NVIDIA/CUDA-9.0/bin${PATH:+:${PATH}}
export DYLD_LIBRARY_PATH=/Developer/NVIDIA/CUDA-9.0/lib\
                         ${DYLD_LIBRARY_PATH:+:${DYLD_LIBRARY_PATH}}

In order to modify, compile, and run the samples, the samples must also be installed with write permissions. A convenience installation script is provided: cuda-install-samples-9.0.sh. This script is installed with the cuda-samples-9-0 package.

$ /Developer/NVIDIA/CUDA-9.0/bin/nsight_ee_plugins_manage.sh install <eclipse-dir>

The CUDA Driver, Toolkit and Samples can be uninstalled by executing the uninstall script provided with each package:

All packages which share an uninstall script will be uninstalled unless the --manifest=<uninstall_manifest> flag is used. Uninstall manifest files are located in the same directory as the uninstall script, and have filenames matching .<package_name>_uninstall_manifest_do_not_delete.txt.

$ cd /Developer/NVIDIA/CUDA-9.0/bin
$ sudo perl uninstall_cuda_9.0.pl \
    --manifest=.cuda_toolkit_uninstall_manifest_do_not_delete.txt

The installation of the compiler is first checked by running nvcc -V in a terminal window. The nvcc command runs the compiler driver that compiles CUDA programs. It calls the host compiler for C code and the NVIDIA PTX compiler for the CUDA code.

make -C 0_Simple/vectorAdd
make -C 0_Simple/vectorAddDrv
make -C 1_Utilities/deviceQuery
make -C 1_Utilities/bandwidthTest

After compilation, go to bin/x86_64/darwin/release and run deviceQuery. If the CUDA software is installed and configured correctly, the output for deviceQuery should look similar to that shown in Figure 1.

Figure 1. Valid Results from deviceQuery CUDA Sample

Note that the parameters for your CUDA device will vary. The key lines are the first and second ones that confirm a device was found and what model it is. Also, the next-to-last line, as indicated, should show that the test passed.

Running the bandwidthTest sample ensures that the system and the CUDA-capable device are able to communicate correctly. Its output is shown in Figure 2

Figure 2. Valid Results from bandwidthTest CUDA Sample

Note that the measurements for your CUDA-capable device description will vary from system to system. The important point is that you obtain measurements, and that the second-to-last line (in Figure 2) confirms that all necessary tests passed.

Should the tests not pass, make sure you have a CUDA-capable NVIDIA GPU on your system and make sure it is properly installed.

If you run into difficulties with the link step (such as libraries not being found), consult the Release Notes found in the doc folder in the CUDA Samples directory.

To see a graphical representation of what CUDA can do, run the particles executable.