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Ask Slashdot: What Is the Most Painless Intro To GPU Programming?
dryriver writes "I am an intermediate-level programmer who works mostly in C# NET. I have a couple of image/video processing algorithms that are highly parallelizable — running them on a GPU instead of a CPU should result in a considerable speedup (anywhere from 10x times to perhaps 30x or 40x times speedup, depending on the quality of the implementation). Now here is my question: What, currently, is the most painless way to start playing with GPU programming? Do I have to learn CUDA/OpenCL — which seems a daunting task to me — or is there a simpler way? Perhaps a Visual Programming Language or 'VPL' that lets you connect boxes/nodes and access the GPU very simply? I should mention that I am on Windows, and that the GPU computing prototypes I want to build should be able to run on Windows. Surely there must a be a 'relatively painless' way out there, with which one can begin to learn how to harness the GPU?"
Learn OpenCL and do the job properly.
This. OpenCL is C based so it shouldn't be that hard to pick up. The efficient algorithms will be basically the same no matter what language or bindings you use.
OpenACC is what you're looking for. It uses a directive based programming model similar to OpenMP, so you write ordinary looking code, then annotate it in ways that tell the compiler how to transform it into GPU code.
You won't get as good performance as well written CUDA or OpenCL code, but it's much easier to learn. And once you get comfortable with it, you may find it easier to make the step from there into lower level programming.
"I'm too busy to research this and form an educated opinion, but I do have time to tell everyone my uninformed opinion."
Writing GPU programs is hard. Not only do you have to learn a new sets of APIs, you also have to understand the underlying architecture to extract decent performance. It requires a different approach to problem solving that requires months if not years to develop.
Fortunately you don't need to read the entire cuda programming guide to program on the GPU. There are several excellent libraries out there which hide the complexities of the GPU architecture. Since you are doing image processing, I would recommend Arrayfire (http://www.accelereyes.com/products/arrayfire). It is a free library which provides several image processing functions which have been optimized for the GPU. You should also look into Thrust and NPP(included with the CUDA toolkit), although these libraries are more verbose and require greater understanding of the C++ and GPU to program.