CUDA Getting Started

University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 0

• (TODO) YOUR NAME HERE
• Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab)

(TODO: Your README)

Include screenshots, analysis, etc. (Remember, this is public, so don't put anything here that you don't want to share with the world.)

Instructions (delete me)

This is due Wednesday, September 2.

Summary: In this project, you will set up your CUDA development tools and verify that you can build, run, and do performance analysis.

This project is a simple program that demonstrates CUDA and OpenGL functionality and interoperability, testing that CUDA has been properly installed. If the machine you are working on has CUDA and OpenGL 4.0 support, then when you run the program, you should see either one or two colors depending on your graphics card.

This project (and all other CUDA projects in this course) requires an NVIDIA graphics card with CUDA capability. Any card with Compute Capability 2.0 (sm_20) or greater will work. Gheck your GPU on this compatibility table. If you do not have a personal machine with these specs, you may use computers in the SIG Lab and Moore 100B/C.

If you need to use the lab computer for your development:

• Make sure that the computer you are on has a supported GPU! The first few machines in each room should support CUDA Compute Capability 2.0. To check the GPU model in a lab computer, open the NVIDIA Control Panel. The GPU model is shown on the initial screen. (NVS 210 will work; GeForce 9300 GE will not.)
• You will not presently be able to do GPU performance profiling. This will be very important for debugging performance bottlenecks in your program. If you do not have administrative access to any CUDA-capable machine, please email the TA.

Part 1: Setting up your development environment

Skip this part if you are developing on a lab computer.

Windows

1. Make sure you are running Windows 7/8/10 and that your NVIDIA drivers are up-to-date. You will need support for OpenGL 4.0 or better in this course.
2. Install Visual Studio 2013 (not 2015).
   • 2010/2012 will also work, if you already have one installed.
   • http://www.seas.upenn.edu/cets/software/msdn/
   • You need C++ support. None of the optional components are necessary.
3. Install CUDA 7.
   • CUDA 7.5 is recommended for its new performance profiling tools. However, 7.0 is fine (and is the version on the lab computers).
   • Use the Express installation. If using Custom, make sure you select Nsight for Visual Studio.
4. Install CMake. (Windows binaries are under "Binary distributions.")
5. Install Git.

OS X

1. Make sure you are running OS X 10.9 or newer.
2. Install XCode (available for free from the App Store).
   • On 10.10, this may not actually be necessary. Try running gcc in a terminal first.
3. Install OS X Unix Command Line Development Tools (if necessary).
4. Install CUDA 7 (don't use cask; the CUDA cask is outdated).
   • CUDA 7.5 is recommended for its new performance profiling tools.
   • Make sure you select Nsight.
5. Install Git (or: brew install git).
6. Install CMake (or: brew cask install cmake).

Linux

Note: to debug CUDA on Linux, you will need an NVIDIA GPU with Compute Capability 5.0.

1. Install CUDA 7.
   • CUDA 7.5 is recommended for its new performance profiling tools.
   • Make sure you select Nsight.
2. Install Git (apt-get install git on Debian/Ubuntu).
3. Install CMake (apt-get install cmake on Debian/Ubuntu).

Part 2: Fork & Clone

1. Use GitHub to fork this repository into your own GitHub account.
2. If you haven't used Git, you'll need to set up a few things.
   • On Windows: In order to use Git commands, you can use Git Bash. You can right-click in a folder and open Git Bash there.
   • On OS X/Linux: Open a terminal.
   • Configure git with some basic options by running these commands:
     • git config --global push.default simple
     • git config --global user.name "YOUR NAME"
     • git config --global user.email "[email protected]"
     • (Or, you can use your own address, but remember that it will be public!)
3. Clone from GitHub onto your machine:
   • Navigate to the directory where you want to keep your 565 projects, then clone your fork.
     • git clone the clone URL from your GitHub fork homepage.

• How to use GitHub
• How to use Git

Part 3: Build & Run

• src/ contains the source code.
• external/ contains the binaries and headers for GLEW and GLFW.

CMake note: Do not change any build settings or add any files to your project directly (in Visual Studio, Nsight, etc.) Instead, edit the src/CMakeLists.txt file. Any files you add must be added here. If you edit it, just rebuild your VS/Nsight project to make it update itself.

Windows

1. In Git Bash, navigate to your cloned project directory.
2. Create a build directory: mkdir build
   • (This "out-of-source" build makes it easy to delete the build directory and try again if something goes wrong with the configuration.)
3. Navigate into that directory: cd build
4. Open the CMake GUI to configure the project:
   • cmake-gui .. or "C:\Program Files (x86)\cmake\bin\cmake-gui.exe" ..
     • Don't forget the .. part!
   • Make sure that the "Source" directory is like .../Project0-CUDA-Getting-Started.
   • Click Configure. Select your version of Visual Studio, Win64. (NOTE: you must use Win64, as we don't provide libraries for Win32.)
   • If you see an error like CUDA_SDK_ROOT_DIR-NOTFOUND, set CUDA_SDK_ROOT_DIR to your CUDA install path. This will be something like: C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v7.5
   • Click Generate.
5. If generation was successful, there should now be a Visual Studio solution (.sln) file in the build directory that you just created. Open this. (from the command line: explorer *.sln)
6. Build. (Note that there are Debug and Release configuration options.)
7. Run. Make sure you run the cis565_ target (not ALL_BUILD) by right-clicking it and selecting "Set as StartUp Project".
   • If you have switchable graphics (NVIDIA Optimus), you may need to force your program to run with only the NVIDIA card. In NVIDIA Control Panel, under "Manage 3D Settings," set "Multi-display/Mixed GPU acceleration" to "Single display performance mode".

OS X & Linux

It is recommended that you use Nsight.

1. Open Nsight. Set the workspace to the one containing your cloned repo.
2. File->Import...->General->Existing Projects Into Workspace.
   • Select the Project 0 repository as the root directory.
3. Select the cis565- project in the Project Explorer. From the Project menu, select Build All.
   • For later use, note that you can select various Debug and Release build configurations under Project->Build Configurations->Set Active....
4. If you see an error like CUDA_SDK_ROOT_DIR-NOTFOUND:
   • In a terminal, navigate to the build directory, then run: cmake-gui ..
   • Set CUDA_SDK_ROOT_DIR to your CUDA install path. This will be something like: /usr/local/cuda
   • Click Configure, then Generate.
5. Right click and Refresh the project.
6. From the Run menu, Run. Select "Local C/C++ Application" and the cis565_ binary.

Part 4: Modify

1. Search the code for TODO: you'll find one in src/main.cpp on line 13. Change the string to your name, rebuild, and run. (m_yourName = "TODO: YOUR NAME HERE";)
2. Take a screenshot of the window (including title bar) and save it to the images directory for Part 6.
3. You're done with some code changes now; make a commit!
   • Make sure to git add the main.cpp file.
   • Use git status to make sure you didn't miss anything.
   • Use git commit to save a version of your code including your changes. Write a short message describing your changes.
   • Use git push to sync your code history to the GitHub server.

Part 5: Analyze

NOTE: This part cannot be done on the lab computers, as it requires administrative access. If you do not have a CUDA-capable computer of your own, you may need to borrow one for this part. However, you can still do the rest of your development on the lab computer.

Windows

1. Go to the Nsight menu in Visual Studio.
2. Select Start Performance Analysis....
3. Select Trace Application. Under Trace Settings, enable tracing for CUDA and OpenGL.
4. Under Application Control, click Launch.
   • If you have switchable graphics (NVIDIA Optimus), see the note in Part 3.
5. Run the program for a few seconds, then close it.
6. At the top of the report page, select Timeline from the drop-down menu.
7. Take a screenshot of this tab and save it to images, for Part 6.

OS X & Linux

1. Open your project in Nsight.
2. Run->Profile.
3. Run the program for a few seconds, then close it.
4. Take a screenshot of the timeline and save it to images, for Part 6.

Part 6: Write-up

1. Update ALL of the TODOs at the top of this README:
   • Remove all of these instructions, so that your README represents your own project, rather than the assignment. You can always read the instructions on the original GitHub page.
   • Add your name, computer, and whether it's a personal or lab computer.
   • Embed the screenshots you took: ![](images/example.png)
   • Syntax help: https://help.github.com/articles/writing-on-github/
2. Add, commit, and push your screenshots and README.
   • Make sure your README looks good on GitHub!
3. If you have modified either of the CMakeLists.txt at all (aside from the list of SOURCE_FILES), you must test your project in Moore 100B.

Submit

1. Open a GitHub pull request so that we can see that you have finished. The title should be "Submission: YOUR NAME".
   • In the body of the pull request, include a link to your repository.
2. Send an email to the TA (gmail: kainino1+cis565@) with:
   • Subject: in the form of [CIS565] Project 0: PENNKEY
   • Direct link to your pull request on GitHub
   • In the form of a grade (0-100+), evaluate your own performance on the project. (N/A for Project 0.)
   • Feedback on the project itself, if any.

And you're done!