Expanding the Farm

If you want to use GPU's to crunch projects, and are a fan of NVIDIA graphics cards, then CUDA is essential, and might as well be your best friend for your crunching projects. CUDA is a parallel computing architecture which makes programs written in higher level languages (C and above, but mostly C) to be able to be executed on a GPU easier than it ever has been before. If you have a CUDA enabled GPU there is one more important bit of information which you should look for. This is the number of CUDA cores. As I do not yet have a GPU crunching projects yet, I am a bit unsure if CUDA cores behave similar to CPU cores for crunching BOINC projects. ( Intuition tells me no, but if it is yes I will be pleasantly surprised, as it is very easy to get GPUs with a substantial number of CUDA cores).

I've spend some time Googling, but sadly I can find no direct comparison for ATI/ AMD graphics cards. But this does not mean that they can not be used for crunching projects. For anyone looking to use GPU's to crunch projects there is this very helpful resource. In case anyone didn't really want to click the link, it sounds like it is mostly any AMD R600 or R700 platform GPU or later.

One last thing to note, not sure it matters with most semi-recent GPU, but for BOINC projects the card will need to have 256MB or memory designated to the card itself. This memory limit is the bare minimum for most projects ( even for standard CPU processing), with some projects needing more than to run on any given processor.

One more small fun fact about GPU and crunching projects. Most commercial CPU on the market today if not all of them top out at less than 100 GFLOPs even with overclocking. While it is easy to find a GPU for around 100 dollars that easily breaks 100 GFLOPs, if not several times that. A small reminder a FLOP is a floating point operation, the small s denotes that its a measurment of how many of them per second, and the G is Giga with the typical meaning of that prefix in the computing world.