diff --git a/README.md b/README.md index e3e2c2b..5c5b1d0 100644 --- a/README.md +++ b/README.md @@ -3,232 +3,20 @@ 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) +* Kangning Li +* Tested on: September 01, 2015 Windows 10, i7-4790 @ 3.6GHz 16GB, GTX 970 4096MB (Personal) 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 +**Summary:** 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](https://developer.nvidia.com/cuda-gpus). If you do not have a personal -machine with these specs, you may use computers in the SIG Lab and Moore -100B/C. - -**HOWEVER**: If you need to use the lab computer for your development, 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](https://developer.nvidia.com/cuda-downloads?sid=925343). - * 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](http://www.cmake.org/download/). (Windows binaries are - under "Binary distributions.") -5. Install [Git](https://git-scm.com/download/win). - -### 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](https://developer.nvidia.com/cuda-downloads?sid=925343) - (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](https://git-scm.com/download/mac) - (or: `brew install git`). -6. Install [CMake](http://www.cmake.org/download/) - (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](https://developer.nvidia.com/cuda-downloads?sid=925343). - * 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 "GITHUB_USER@users.noreply.github.com"` - * (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](https://guides.github.com/activities/hello-world/) -* [How to use Git](http://git-scm.com/docs/gittutorial) - - -## 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" ..` - * 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. +Screenshots include a shot of the program running with my name in the title bar and a shot of the Nsight profiler's output within Visual Studio 2013. -## Submit -1. Open a GitHub pull request so that we can see that you have finished. - The title should be "Submission: YOUR NAME". -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. +![](images/Screenshot_1.png) -And you're done! +![](images/Screenshot_2.png) diff --git a/images/Screenshot_1.png b/images/Screenshot_1.png new file mode 100644 index 0000000..29c282a Binary files /dev/null and b/images/Screenshot_1.png differ diff --git a/images/Screenshot_2.png b/images/Screenshot_2.png new file mode 100644 index 0000000..010d83d Binary files /dev/null and b/images/Screenshot_2.png differ diff --git a/src/main.cpp b/src/main.cpp index a51deff..3fa1de9 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -11,7 +11,7 @@ */ int main(int argc, char* argv[]) { // TODO: Change this line to use your name! - m_yourName = "TODO: YOUR NAME HERE"; + m_yourName = "Kangning Li"; if (init(argc, argv)) { mainLoop();