1. Introduction

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).

CUDA was developed with several design goals in mind:
  • 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.
CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. These cores have shared resources including a register file and a shared memory. The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus.

This guide will show you how to install and check the correct operation of the CUDA development tools.

1.1. System Requirements

To use CUDA on your system, you need to have:
  • 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)
Table 1. Mac Operating System Support in CUDA 6.0
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.

1.2. About This Document

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.

2. Prerequisites

2.1. CUDA-capable GPU

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.

2.2. Mac OS X Version

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.

Command-Line Tools

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.

Once Xcode is installed, the command-line tools can be installed by launching Xcode and following those steps:
  1. Xcode > Preferences... > Downloads > Components
  2. 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.

3. Installation

3.1. Download

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.

To calculate the MD5 checksum of the downloaded file, run the following:
$ openssl md5 <file>

3.2. Install

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.

Choose which packages you wish to install. The packages are:
  • 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.
Set up the required environment variables:
export PATH=/Developer/NVIDIA/CUDA-6.0/bin:$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.

Note: To run CUDA applications in console mode on MacBook Pro with both an integrated GPU and a discrete GPU, use the following settings before dropping to console mode:
  1. Uncheck System Preferences > Energy Saver > Automatic Graphic Switch
  2. Drag the Computer sleep bar to Never in System Preferences > Energy Saver

3.3. Uninstall

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

4. Verification

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.

Note: Ensure the PATH and DYLD_LIBRARY_PATH variables are set correctly.

4.1. Driver

If the CUDA Driver is installed correctly, the CUDA kernel extension (/System/Library/Extensions/CUDA.kext) should be loaded automatically at boot time. To verify that it is loaded, use the command
kextstat | grep -i cuda

4.2. Compiler

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.

Note: On Mac OS 10.8 with XCode 5, nvcc must be invoked with --ccbin=path-to-clang-executable. There are some features that are not yet supported: Clang language extensions (see http://clang.llvm.org/docs/LanguageExtensions.html), LLVM libc++ (only GNU libstdc++ is currently supported), language features introduced in C++11, the __global__ function template explicit instantiation definition, and 32-bit architecture cross-compilation.
The NVIDIA CUDA Toolkit includes CUDA sample programs in source form. To fully verify that the compiler works properly, a couple of samples should be built. After switching to the directory where the samples were installed, type:
make -C 0_Simple/vectorAdd
make -C 0_Simple/vectorAddDrv
make -C 1_Utilities/deviceQuery
make -C 1_Utilities/bandwidthTest
The 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.

4.3. Runtime

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

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

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.

5. Additional Considerations

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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