Intro to GPU programming [closed]
Everyone has this huge massively parallelized supercomputer on their desktop in the form of a graphics card GPU.
- What is the "hello world" equivalent of the GPU community?
- What do I do, where do I go, to get started programming the GPU for the major GPU vendors?
-Adam
Check out CUDA by NVidia, IMO it's the easiest platform to do GPU programming. There are tons of cool materials to read.
http://www.nvidia.com/object/cuda_home.html
Hello world would be to do any kind of calculation using GPU.
Hope that helps.
- You get programmable vertex and pixel shaders that allow execution of code directly on the GPU to manipulate the buffers that are to be drawn. These languages (i.e. OpenGL's GL Shader Lang and High Level Shader Lang and DirectX's equivalents ), are C style syntax, and really easy to use. Some examples of HLSL can be found here for XNA game studio and Direct X. I don't have any decent GLSL references, but I'm sure there are a lot around. These shader languages give an immense amount of power to manipulate what gets drawn at a per-vertex or per-pixel level, directly on the graphics card, making things like shadows, lighting, and bloom really easy to implement.
- The second thing that comes to mind is using openCL to code for the new lines of general purpose GPU's. I'm not sure how to use this, but my understanding is that openCL gives you the beginnings of being able to access processors on both the graphics card and normal cpu. This is not mainstream technology yet, and seems to be driven by Apple.
- CUDA seems to be a hot topic. CUDA is nVidia's way of accessing the GPU power. Here are some intros
I think the others have answered your second question. As for the first, the "Hello World" of CUDA, I don't think there is a set standard, but personally, I'd recommend a parallel adder (i.e. a programme that sums N integers).
If you look the "reduction" example in the NVIDIA SDK, the superficially simple task can be extended to demonstrate numerous CUDA considerations such as coalesced reads, memory bank conflicts and loop unrolling.
See this presentation for more info:
http://www.gpgpu.org/sc2007/SC07_CUDA_5_Optimization_Harris.pdf