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The NVIDIA Visual Profiler is a cross-platform performance profiling tool that delivers developers vital feedback for optimizing CUDA C/C++ applications. First introduced in 2008, Visual Profiler supports all 350 million+ CUDA capable NVIDIA GPUs shipped since 2006 on Linux, Mac OS X, and Windows. The NVIDIA Visual Profiler is available as part of the CUDA Toolkit.

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Note that NVIDIA® CUDA Toolkit 11.0 (and later) no longer supports development or running applications on macOS. While there are no tools which use macOS as a target environment, NVIDIA is making a macOS host version of Visual Profiler available from which you can launch profiling sessions on supported target platforms. You may download this and other macOS tools using the button below.

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Overview

  • Focus on the information that matters
    Quickly identify potential performance bottleneck issues in your applications using highly configurable tables and graphical views
  • Automated performance analysis
    Perform automated analysis of your application to identify performance bottlenecks and get optimization suggestions that can be used to improve performance
  • Unified CPU and GPU Timeline
    View CUDA activity occurring on both CPU and GPU in a unified time line, including CUDA API calls, memory transfers and CUDA launches.
  • CUDA API trace
    View all memory transfers, kernel launches, and other API functions on the same timeline
  • Drill down to raw data
    Gain low-level insights by looking at performance metrics collected directly from GPU hardware counters and software instrumentation.
  • Compare results across multiple sessions
    Confirm performance improvements by comparing against previous sessions
  • Analyze data collected from remote systems
    Use the command line profiler using environment variables to collect data from multiple systems and analyze the results in Visual Profiler
  • CUDA Dynamic Parallelism
    View timeline for applications that use CUDA Dynamic Parallelism including both host-launched and device-launched kernels and the parent-child relationship between kernels.
  • Guided Application Analysis
    Use the guided analysis mode has to get step-by-step analysis and optimization guidance. The analysis results now include graphical visualizations to more clearly indicate the optimization opportunities.
  • Power, thermal, and clock profiling
    Observe how GPU power, thermal, and clock values vary during application execution

The latest version of Visual Profiler with support for both CUDA C/C++ applications is available with the CUDA Toolkit and is supported on all platforms supported by the CUDA Toolkit.

Developers should be sure to check out NVIDIA Nsight Systems for our next generation profiling tool with Linux, Windows, macOS, PowerPC, and Arm support. Be sure to review our tool migration recommendations to make your transition easier.

For development and debugging on Windows, see Nsight Visual Studio Edition and NVIDIA Nsight Systems Visual Studio integration with NVIDIA Nsight Integration.

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For more information on the Visual Profiler and other CUDA development tools:

  • NVIDIA Nsight Systems
    • NVIDIA Nsight Systems Visual Studio integration with NVIDIA Nsight Integration

Questions on CUDA Tools?

If you encounter difficulty with any of the CUDA Tools or have more questions please contact the NVIDIA tools team at (cudatools@nvidia.com).

Tutorials
This tutorial will demonstrate how you can install Anaconda, a powerful package manager, on your Mac.

Anaconda is a package manager, an environment manager, and Python distribution that contains a collection of many open source packages. An installation of Anaconda comes with many packages such as numpy, scikit-learn, scipy, and pandas preinstalled and is also the recommended way to install Jupyter Notebooks. This tutorial will include:

With that, let’s get started

Graphical Installation of Anaconda

Installing Anaconda using a graphical installer is probably the easiest way to install Anaconda.

1 ‒ Go to the Anaconda Website and choose a Python 3.x graphical installer (A) or a Python 2.x graphical installer (B). If you aren’t sure which Python version you want to install, choose Python 3. Do not choose both.

2 - Locate your download and double click it.

3 - Click on Continue

4 - Click on Continue

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5 - Note that when you install Anaconda, it modifies your bash profile with either anaconda3 or anaconda2 depending on what Python version you choose. This can important for later. Click on Continue.

6 - Click on Continue to get the License Agreement to appear.

You will need to read and click Agree to the license agreement before clicking on Continue again.

7 - Click on Install

8 - You’ll be prompted to give your password, which is usually the one that you also use to unlock your Mac when you start it up. After you enter your password, click on Install Software.

9 - Click on Continue. You can install Microsoft Visual Studio Code if you like, but it is not required. It is an Integrated Development Environment. You can learn about Python Integrated Development Environments here.

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10 - You should get a screen saying the installation has completed. Close the installer and move it to the trash.

Test your Installation

1 - Open a new terminal on your Mac. You can do this by clicking on the Spotlight magnifying glass at the top right of the screen, type “terminal” then click on the terminal icon. Now, type the following command into your terminal

If you had chosen a Python 3 version of Anaconda (like the one in the image above), you will get an output similar to above.

If you had chosen a Python 2 version of Anaconda, you should get a similar output to the one below.

2 - Another good way to test your installation is to try and open a Jupyter Notebook. You can type the command below in your terminal to open a Jupyter Notebook. If the command fails, chances are that Anaconda isn’t in your path. See the next section on Common Issues.

The image below shows a Jupyter Notebook in action. Jupyter notebooks contain both code and rich text elements, such as figures, links, and equations. You can learn more about Jupyter Notebooks here.

Common Issues

The image below shows step 5 of the Graphical Installation of Anaconda from earlier in this tutorial. Notice that when you install Anaconda, it modifies your .bash_profile to put Anaconda in your path.

The problem is that sometimes people have installed multiple different versions of Anaconda and consequently they have multiple versions of Anaconda in their path. For example, say a person needs Python 2 and they install a Python 2 version of Anaconda, That same person then finds that they need Python 3, so they install a Python 3 version of Anaconda. The problem is that you really only need 1 version of Anaconda. A lot of people think that is that if you install a Python 2 version of Anaconda, you are stuck with Python 2. Anaconda is also an environment manager and makes it very easy to go back and forth between Python 2 and 3 on a single installation of Anaconda (learn more here).

To see if you have more than 1 version of anaconda installed and to fix it if you do, let’s first look at your .bash_profile.

1 - Open a new terminal and go to your home directory. You can do this by using the command below.

2 - Use the cat command to see the contents of the hidden file .bash_profile. Type the following command into your terminal.

You should only see one anaconda version added to your path as you see below, this isn’t a problem for you. Move to the conclusion of the tutorial.

If you see more than one Anaconda version, proceed to step 3.

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3 - To remove the old version of Anaconda from your .bash_profile use the command below to edit the file using the nano editor.

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From the image above, notice there is a newer Version of Anaconda. Simply remove the older version of Anaconda. Type control + X to exit out of nano.

Save changes by typing Y.

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Close that terminal and open a new one. You should be okay now. Keep in mind that this isn’t the only issue you can have when installing Anaconda, but it is a very common issue.

Conclusion

This tutorial provided a quick guide to install Anaconda on Mac as well as dealing with a common installation issue. A graphical install of Anaconda isn’t the only way to install Anaconda as you can Install Anaconda by a Command Line Installer, but it is the easiest. If you aren’t sure what to do after installing Anaconda, here are a few things you can do:

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  • If you would like to learn more about Anaconda, you can learn about more here.
  • If you want to start coding on your local computer, you can check out the the Jupyter Notebook Definitive Guide to learn how to code in Jupyter Notebooks.
  • If you want to learn Python, you can check out DataCamp's Intro to Python for Data Science course.

If you any questions or thoughts on the tutorial, feel free to reach out in the comments below or through Twitter.

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