i have now a quadro fx1700 graphics card and after some orientations in cuda i want to buy a new one for computation using GPU. In the Appendix of Programming guide (2.3.1) there is a table of performances of different nvida cards. It seems the GTX295 is one of the best cuda enabled devices. But I have still some questions :rolleyes: :
Do the three brands of nvidia play any roll for choosing a GPGPU? Is that necessary to buy a Telsa series card? (it is very expensive…)
Is the GTX295 a good choice as GPGPU? Are there some else graphics cards better than it in this area with a economical cost (for example Quadro FX 5800)?
thx for your reply! Why cant I put more Tesla cards on one desktop? I thought the c1060 is a simple combination of GeForce 200 chips on one graphics card.
thx for ur reply. Can u explan it particularly please? Why did u mind with a quadro card i can play more for the same thing.
Maybe i haven’t written my requirement here clearly: i need a good graphics card for parallel computation of “normal” algrithms, not for playing games…
If you have some time you can wait for a Fermi based Geforce card. Meanwhile you can buy a “small” GT200 based card or simply wait.
It depends if you use this card for development purposes or for running the algorithms in a “big scale”.
I would even suggest to use 2 graphics card and dedicate one for CUDA and the other for the display output. With the GT200 you need a second card to use hardware debugging and since the emulation support will be discontinued with the release of CUDA 3.0 you will need it to debug your kernels.
The next thing is that you have to develope your programs explicitly to use 2 graphics cards (GTX 295). It all depends on your needs.
Thx for ur reply! I’ve read the website of nvidia fermi. Is the definition of “cuda cores” identical with Multiprocessor? (1 Multiprocessor has 8 Processors and a GTX295 has 2*30 MP). Then the fermi is a great evolution.
“Cuda cores” means the processing elements on a “streaming multiprocessor” SM.
On the GT200 you have 30 streaming multiprocessors with 8 cuda cores on each SM, this equals 240 cuda cores.
On Fermi (GF100) you have 16 streaming multiprocessors with 32 cuda cores - 512 cuda cores.
I’m already waiting for the GF100, at least for detailed specs or new release of the CUDA programming guide, that includes the GF100 architecture. In my opinion it will be be a great card for GPGPU. Hardware debugging support with one GPU, Cache, dual DMA-Engine, fast double precision, fast integer support (fast modulo, multiplication and division), C++ support, 64-Bit memory architecture.
I also assume that some shortcomings of the current architecture will be corrected, like 3D grids, texture writes, 3D linear memory, additional texture adressing and probably much more.
One thing is missing until now, that would bring more companies to use CUDA in their programs. An official compiler that translates PTX-to-x86 - like Ocelot, but with Nvidia support and for all platforms. It would avoid developing the same algorithms for x86 and for CUDA.
Today I found an interesting paper, which shows that Nvidia is probably also interested in this topic
what did u mean with that? Why will the emulation support be discontinued with 3.0? How can screen one of the cards for video output and how can I use the second card for hardware debugging?