Nvidia cuda mac os ��

CUDA В® 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).

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

1.1. System Requirements

Table 1. Mac Operating System Support in CUDA 9.0

Toolchain		Mac OSX Version (native x86_64)
Xcode	Apple LLVM	10.12
8.3.3	8.1.0	YES

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 вЂ¦ 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. 10.12. To check which version you have, go to the Apple menu on the desktop and select About This Mac .

Xcode Version

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 .

Command-Line Tools

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.

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

3. Installation

Basic instructions can be found in the Quick Start Guide. Read on for more detailed instructions.

3.1. Download

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.

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.

: Specifies a package to install. Can be used multiple times. Options are «cuda-toolkit», «cuda-samples», and «cuda-driver».
—log-file=

: Specify a file to log the installation to. Default is /var/log/cuda_installer.log.

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.

3.3. Uninstall

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

Table 2. Mac Uninstall Script Locations

Package	Location
CUDA Driver	/usr/bin/uninstall_cuda_drv.pl
CUDA Toolkit	/Developer/NVIDIA/CUDA- 9.0 /bin/uninstall_cuda_ 9.0 .pl
CUDA Samples	/Developer/NVIDIA/CUDA- 9.0 /bin/uninstall_cuda_ 9.0 .pl

All packages which share an uninstall script will be uninstalled unless the —manifest= flag is used. Uninstall manifest files are located in the same directory as the uninstall script, and have filenames matching .

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.

4.1. Driver

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.

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.

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.

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 В® Nsightв„ў Eclipse Edition, NVIDIA Visual Profiler, cuda-gdb, and cuda-memcheck.

For technical support on programming questions, consult and participate in the Developer Forums.

Notices

Notice

ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, «MATERIALS») ARE BEING PROVIDED «AS IS.» NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.

Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication of otherwise under any patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all other information previously supplied. NVIDIA Corporation products are not authorized as critical components in life support devices or systems without express written approval of NVIDIA Corporation.

Trademarks

NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.

Copyright

Источник

NVIDIA CUDA Toolkit — Developer Tools for macOS — CUDA Toolkit 11.4 Update 1

NVIDIA® CUDA Toolkit 11.4 Update 1 no longer supports development or running applications on macOS. While there are no tools which use macOS as a target environment, NVIDIA is making macOS host versions of these tools that you can launch profiling and debugging sessions on supported target platforms.

You may download all these tools here. Note that the Nsight tools provide the ability to download these macOS host versions on their respective product pages.

Please visit each tool’s overview page for more information about the tool and its supported target platforms.

The macOS host tools provided are:

Nsight Systems — a system profiler and timeline trace tool supporting Pascal and newer GPUs
Nsight Compute — a CUDA kernel profiler supporting Volta and new GPUs
Visual Profiler — a CUDA kernel and system profiler and timeline trace tool supporting older GPUs (see installation instructions, below)
cuda-gdb — a GPU and CPU CUDA application debugger (see installation instructions, below)

Revision History

NVIDIA® development tools are freely offered through the NVIDIA Registered Developer Program

Instructions for installing cuda-gdb on the macOS

Create an installation directory
Download the cuda-gdb-darwin-11.4.100.tar.gz tar archive into $INSTALL_DIR above
Unpack the tar archive
Add the bin directory to your path
Run cuda-gdb —version to confirm you’re picking up the correct binaries
You should see the following output:
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Nvidia cuda mac os ��� ���