Currently I am running the following for my system.
CPU: AMD Phenom 9950 overclocked to 3.0GHz
RAM: 8GB DDR2 800MHz
Video Card: Geforce 9600GT 2GB
I am currently doing FDTD work with large matrices in MATLAB and would like to exploit the GPU to speed things up. I’m on a tight budget so have been looking into the gtx 550 and 560 cards.
Can someone make a solid recommendation on what card would work best for me. I was hoping the 600 series would be something I should look at but the review on Tom’s Hardware said that the compute capabilities are being scaled back in the gaming graphics cards. Is this really true? and does that mean I should look at older generation cards like the 400 and 500 series?
Also I am trying to stay below $300, but if the additional performance is there I am willing to go higher.
This makes a difference depending on your ratio of arithmetic operations to memory reads and writes. Many CUDA algorithms are memory bandwidth bound, so you should figure out if your intended application clearly bandwidth bound.
That said, without knowing anything about your specific application, I’ve never regretted paying for more memory bandwidth. :)
SLI does not have any impact on CUDA. With two cards in SLI mode, you will still see two separate CUDA devices with their own memory space, and memory transfers between devices will still use the PCI-Express bus. You will have to copy data between the devices manually, or possibly look into unified virtual addressing to streamline memory copies between devices. (UVA might only work for GeForce cards in Linux, I can’t recall at the moment…)