NVIDIA CUDA GETTING STARTED GUIDE
FOR MAC OS X
DU-05348-001_v6.5 | August 2014
Installation and Verification on Mac OS X
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NVIDIA CUDA Getting Started Guide for Mac OS X DU-05348-001_v6.5|ii
TABLE OF CONTENTS
Chapter 1. Introduction.........................................................................................1
1.1.System Requirements.................................................................................... 1
1.2.About This Document.................................................................................... 2
Chapter2.Prerequisites........................................................................................3
2.1.CUDA-capable GPU....................................................................................... 3
2.2.Mac OS X Version......................................................................................... 3
2.3.Command-Line Tools..................................................................................... 3
Chapter 3. Installation.......................................................................................... 5
3.1. Download................................................................................................... 5
3.2. Install....................................................................................................... 5
3.3. Uninstall.................................................................................................... 6
Chapter 4. Verification..........................................................................................7
4.1. Driver....................................................................................................... 7
4.2. Compiler....................................................................................................7
4.3. Runtime.....................................................................................................8
Chapter5.Additional Considerations...................................................................... 10
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Chapter1.
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)
Introduction
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Table1 Mac Operating System Support in CUDA 6.5
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.
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Chapter2.
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.
2.3.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.
Prerequisites
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NVIDIA CUDA Getting Started Guide for Mac OS X DU-05348-001_v6.5|4
You can verify that the toolchain is installed by entering the command /usr/bin/cc --
help from a Terminal window.
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Chapter3.
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-6.5 by default. Symlinks are created in /usr/local/cuda/
pointing to their respective files in /Developer/NVIDIA/CUDA-6.5/. Previous
Installation
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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-6.5/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-6.5/bin:$PATH
export DYLD_LIBRARY_PATH=/Developer/NVIDIA/CUDA-6.5/lib:$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-6.5.sh. This script is installed with the cuda-samples-6-5 package.
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:
/Developer/NVIDIA/CUDA-6.5/bin/uninstall
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Chapter4.
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.
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.
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,
and the __global__ function template explicit instantiation definition.
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:
Verification
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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.
Figure1 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
Verification
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Figure2 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.
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Chapter5.
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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