That stands out more sense wise than the other comments, ie PrimeGrid is OpenCL and uses all of the Radeon processing capability on one wu. Where Help Conquer Cancer one wu didn't tax the Radeon GPU.HaloJones wrote:(Transposing sentence.) Soon that will change and your 7950 will get ten times more points than you're seeing. And as for the points you're seeing, with the standard production clients, Nvidia has historically had better support for Folding than ATI/AMD. .
Other peoples comments are I think from the side of Nvidia which I agree will only process one wu at a time.
However from my comments previous and below the Radeon behaves differently in Open CL.
However the comment of using Open CL with Nvidia is puzzling because my hands on usage on GPU cards in the $300 range Nvidia is ~1/4-1/5 as slow comparing a Galaxy factory o/c 660 Ti to a HD 7950 in Open CL. I have heard of FahCore 17 but just the mention of it. Also of notice for comments later you can NOT run two wu of Help Conquer Cancer on an Nvidia and get significant decrease of run times divided by two but you can run the two work units at the same time with a slight offset and both will process and finish. Finishing times on the Nvidia totaled longer, it didn't reduce the time for completion like the Radeon GPU.
The following quote doesn't always hold true:
As you have noticed in my previous comments I have run 14 parallel wu in one program which decreased times down to 30 seconds divided by the total of work units. The most to be effective was 8 or 9 max wu at the same time. Again this is with the HD 7950 not a Nvidia cards as described above.
"You're misunderstanding the architecture here. GPUs don't have any concept of "threads". GPUs parallelize the work that they are given, and most of the tasks that they are given are embarrassingly parallel. For example, in computer graphics, a GPU can render a polygon-based model at 60 FPS or faster because it has the ability to work with every vertex at the same time, and then every pixel/fragment in between at the same time. With F@h, a request for calculations comes in, is divided up and highly parallelized across all GPU cores, and is thus completed in parallel."