Skip to content

Commit

Permalink
Update README.md
Browse files Browse the repository at this point in the history
  • Loading branch information
@arizonat
arizonat committed Aug 30, 2015
1 parent fe14ed5 commit a23cca8
Showing 1 changed file with 3 additions and 204 deletions.
207 changes: 3 additions & 204 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -3,213 +3,12 @@ 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)
* Levi Cai
* Tested on: Windows 7, i7-4770 @ 3.40GHz 16GB, NVidia NVS 310 512MB (Moore 100C Lab)

### (TODO: Your README)
![](images/hw0_screenshot.png)

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](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 7.5](https://developer.nvidia.com/cuda-downloads?sid=925343).
* CUDA 7.5 is recommended for its new performance profiling tools.
* Use the Express installation. If using Custom, make sure you select
Nsight for Visual Studio.
4. Install [CMake](http://www.cmake.org/download/).
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).
* Make sure you get 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).
* Make sure you get 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](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".

### OS X & Linux

It is recommended that you use Nsight.

1. In a terminal, navigate to your cloned project directory
(`cd some/path`).
2. Type `make`.
3. If you see an error like `CUDA_SDK_ROOT_DIR-NOTFOUND`:
* `cd` to the build directory, then run CMake GUI: `cmake-gui ..`
* Set `CUDA_SDK_ROOT_DIR` to your CUDA install path.
This will be something like: `/usr/local/cuda`
* Click *Configure*, then *Generate*.
4. Open Nsight. Set the workspace to the one *containing* your cloned repo.
5. *File->New->Makefile Project with Existing Code*.
* Set the *Existing Code Location* to the cloned project directory.
* Select *CUDA Toolkit*.
* *Finish*.
6. Select the *cis565-* project in the Project Explorer. From the *Project*
menu, select *Build All*.
7. From the *Run* menu, *Run*. Select 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*.
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.
Expand Down

0 comments on commit a23cca8

Please sign in to comment.