CUDA™ is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).
- Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation.
- Support heterogeneous computation where applications use both the CPU and GPU. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. As such, CUDA can be incrementally applied to existing applications. The CPU and GPU are treated as separate devices that have their own memory spaces. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources.
This guide will show you how to install and check the correct operation of the CUDA development tools.
- a CUDA-capable GPU
- Mac OS X 10.8 or later
- the gcc or Clang compiler and toolchain installed using Xcode
- the NVIDIA CUDA Toolkit (available from the CUDA Download page)
|Operating System||Native x86_64||GCC||Clang|
|Mac OS X 10.9.x||YES||5.0, 4.2|
|Mac OS X 10.8.x||YES||4.2.1||5.0|
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. 10.8 or later. To check which version you have, go to the Apple menu on the desktop and select About This Mac.
The CUDA Toolkit requires that the native command-line tools (gcc, clang,...) are already installed on the system.
To install those command-line tools, Xcode must be installed first. Xcode is available from the Mac App Store.
- Install the Command Line Tools package
Alternatively, you can install the command-line tools from the Terminal window by typing the following command: xcode-select --install.
You can verify that the toolchain is installed by entering the command /usr/bin/cc --help from a Terminal window.
Once you have verified that you have a supported NVIDIA GPU, a supported version the MAC OS, and gcc, you need to download the NVIDIA CUDA Toolkit.
The NVIDIA CUDA Toolkit is available at no cost from the main CUDA Downloads page. It contains the driver and tools needed to create, build and run a CUDA application as well as libraries, header files, CUDA samples source code, and other resources.
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.
- CUDA Driver: This will install /Library/Frameworks/CUDA.framework and the UNIX-compatibility stub /usr/local/cuda/lib/libcuda.dylib that refers to it.
- CUDA Toolkit: The CUDA Toolkit supplements the CUDA Driver with compilers and additional libraries and header files that are installed into /Developer/NVIDIA/CUDA-6.0 by default. Symlinks are created in /usr/local/cuda/ pointing to their respective files in /Developer/NVIDIA/CUDA-6.0/. Previous installations of the toolkit will be moved to /Developer/NVIDIA/CUDA-#.# to better support side-by-side installations.
- CUDA Samples (read-only): A read-only copy of the CUDA Samples is installed in /Developer/NVIDIA/CUDA-6.0/samples. Previous installations of the samples will be moved to /Developer/NVIDIA/CUDA-#.#/samples to better support side-by-side installations.
export PATH=/Developer/NVIDIA/CUDA-6.0/bin:$PATH export DYLD_LIBRARY_PATH=/Developer/NVIDIA/CUDA-6.0/lib:$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-6.0.sh. This script is installed with the cuda-samples-6-0 package.
- Drag the Computer sleep bar to Never in
Before continuing, it is important to verify that the CUDA toolkit can find and communicate correctly with the CUDA-capable hardware. To do this, you need to compile and run some of the included sample programs.
kextstat | grep -i cuda
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/bandwidthTestThe builds should produce no error message. The resulting binaries will appear under <dir>/bin/x86_64/darwin/release. To go further and build all the CUDA samples, simply type make from the samples root directory.
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.
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
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.
Now that you have CUDA-capable hardware and the NVIDIA CUDA Toolkit installed, you can examine and enjoy the numerous included programs. To begin using CUDA to accelerate the performance of your own applications, consult the CUDA C Programming Guide.
A number of helpful development tools are included in the CUDA Toolkit to assist you as you develop your CUDA programs, such as NVIDIA® Nsight™ Eclipse Edition, NVIDIA Visual Profiler, cuda-gdb, and cuda-memcheck.
For technical support on programming questions, consult and participate in the Developer Forums.
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