This tutorial series is aimed at developers trying to learn OpenCL from the bottom up, with a focus on practicality. This installment introduces OpenCL context sharing with OpenGL. We make a simple particle system to demonstrate this feature. One of the most important aspects of this feature is the time we can save by doing rendering and calculations on the same memory in the GPU, this means we don’t need to copy data back and forth!

You may want to grab the code and compile it to see it in action. As usual I recommend having a copy of the OpenCL specification handy.

In this tutorial I use the C++ bindings with less explanation, so see Part 1.5 for a more in-depth explanation or Part 1 to get started with C bindings. As before this code has been tested by me on NVIDIA hardware on my Macbook Pro and on an Ubuntu workstation, ATI and Windows users are encouraged to try it out and let me know if you have any problems so I can update the tutorial.

Sharing is Caring!

Lets see what we need to do to get OpenCL and OpenGL sharing their context. The first thing we need is an OpenGL context! For this tutorial I use GLUT to create a window that we can draw in, and GLEW for OpenGL extensions (at least on linux). This means you will need to have those headers and libraries installed on your system. Once you have those you can try building:

cd part2
mkdir build
cd build
cmake ..
make

The Source Code Files

Let’s go over the source files again, even though they are the same as the last tutorial we add more functionality in a couple of them, so it will be good to quickly go over the changes.

main.cpp
This is where we test out our CL class. We setup a GLUT window and OpenGL context. Then we instantiate our CL class, prepare a simple particle system and initialize it. At the end of the file are several helper functions for manipulating the OpenGL view with the mouse and keyboard.

cll.h
The main header file for our CL class definition, also handles including the OpenCL libraries. I’ve downloaded the header files from the Khronos website to avoid having to search the computer for a particular SDK. Note that I’ve had to make a slight change to cl.hpp for Mac users because of a bug in the implementation, which I will cover later.

cll.cpp
The core implementation of our CL class, including functions for initializing the OpenCL context from OpenGL, loading and building an OpenCL program.

part2.cpp
Implementation of the functions that setup and run the OpenCL kernel. This is where we actually see OpenCL in action.

part2.cl
The actual OpenCL code to be executed. Right now it’s a simple particle system that models gravity.

util.h and util.cpp
Utility functions that make things like creating VBOs or printing out OpenCL error messages easier

CMakeLists.txt
The configuration and build script used to build the project. This makes it easier to be portable, and building our code as a library makes it easier to contribute to other projects.

The Source Code Contents

So our main.cpp is a bit messier than before, this is mostly just setting up the OpenGL stuff. You may already have some OpenGL context to plug your OpenCL stuff into, in which case you just want to make sure you are using Vertex Buffer Objects (VBOs) that OpenCL can use to create its buffers.
I will just point out some interesting bits that are important to OpenCL, starting with the number of particles and a pointer to our CL class:

#define NUM_PARTICLES 10000
CL* example;

and later we instantiate it like:

 example = new CL();

We do this so that we can access the object from the GLUT loop, specifically in the appRender function which is called by GLUT to update the display. Before we can talk about rendering though, we need to load some data! Take a look at the for loop where we populate three vectors of Vec4 which is just a typedef for 4 floats; x, y, z, w, declared in cll.h. We then pass these to our class which will push the data to the GPU.

example->loadData(pos, vel, color);
example->popCorn();

Lastly in main.cpp let’s take a look at the void appRender() function, where the first thing we do is update the particle system:

example->runKernel();

This is followed by the rendering code, which is standard OpenGL for drawing points from a VBO. I learned this from here.

Now let’s took at some changes to cll.cpp.
It is easier to just click the link and view the whole source file, since the code is too long for a snippet here, but the major addition is the way we ceate the context. Each operating system uses different extensions to accomplish the same thing, the code mostly comes from the NVIDIA GPU SDK examples, but I modified it to use the C++ bindings. In doing so I found an inconsistency in Apple’s implementation and had to add an extra constructor to the Context class in cl.hpp (around line 1448) to compensate.

We see the most OpenCL action in part2.cpp which also demonstrates creating a CL buffer from a GL buffer. First we create the GL buffer as a VBO

p_vbo = createVBO(&pos[0], array_size, GL_ARRAY_BUFFER, GL_DYNAMIC_DRAW);

The nice thing about std::vectors is that they store their elements in a tightly packed array so we can just pass the address of the first element. Next we store our vbos in another vector as cl::BufferGL objects:

cl_vbos.push_back(cl::BufferGL(context, CL_MEM_READ_WRITE, p_vbo, &err));

We don’t need to push any data to them like we do our pure OpenCL buffers because they just reference the data that is already in the VBO!

Our popCorn function just loads the kernel like before, and sets its arguments. Finally in the runKernel function we have to do a couple extra things to work with our VBOs, namely

err = queue.enqueueAcquireGLObjects(&cl_vbos, NULL, &event);

and

err = queue.enqueueReleaseGLObjects(&cl_vbos, NULL, &event);

Which acquires the buffers before we execute the kernel, and then releases them when we are done. This way we can safely work on the data without interfering with OpenGL or it interfering with our OpenCL.

The last thing to talk about is the cl code itself, but I’ve heavily commented the code so it will be easier to just go read the file :)

I hope this tutorial helps, please let me know if I’m mistaken anywhere so I can correct it for others reading! I’m learning a lot about particle systems for my master’s thesis at the Florida State University Department of Scientific Computing!