Developing with OpenCl on ATI and Nvidia on the same time

our workgroup is slowly trying a little bit of OpenCl in a side project. So far 'everybody' is working on NVIDIA Quadro FX 580. Now we are planning to buy new computers for new colleages and instead of the FX 580 we could buy ATI FirePro V4800 instead, which costs only 15Eur more and give us 1Gig instead of 512Gig of Ram which will benificial for our data intensive tasks.

So, how much trouble is it to develop OpenCl code at the same time on Nvidia and ATI?

I read the following SO question, Running OpenCL on hardware from mixe开发者_运维问答d vendors, which was very pessimistic about developing on/for different vendors. On the other side, the question is already a year old.

What do you reccomend?

I have previous worked extensively with CUDA programming language.

I have been planning to start developing apps using OpenCL. As you mentioned one of the best features with OpenCL is running on many vendor hardware (Intel, AMD and Nvidia).

One project that I came across that used openCL extensively for large scale development is http://sourceforge.net/projects/hypgad/. It might be a good idea to look at the source code from this group and understand how they have developed their application on so many hardware including sony cell processor.

Another approach would be to use PyOPENCL, which provides higher abstraction than OpenCL and can significantly reduce the coding effort.

Do you need the code to run unchanged on both bits of hardware? If so you may have to develop for a limited subset of common functions.

If you can run slightly different c ode on each you will probably get better performance - in CUDA/OpenCL you generally have to tune the algorithms for the amount of ram, number of GPU engines anyway so it shoudldn't be much more work to also tweak for NVidia/AMD

The biggest problem is workgroup sizes. Some ATI cards I have used crash at above 64, but then it may be the Apple OSX 10.6 drivers I am using.

Developing for both ATI and NVIDIA is actually not too difficult so long as you avoid using any part of either vendor's SDK. Stick to OpenCL as it is defined in the OpenCL spec. (www.khronos.org/opencl) and your code will stay syntax portable. Due to differences in the underlying architectures, performance portability may be an issue. Local & Global worksizes really have to be determined independently for each card to maximize performance. Another thing to pay attention to is the types being used. Vector types (float2, float4) are especially useful on ATI cards, as each processing element actually contains 4 execution units (one for each RGB color channel, plus aplha).