On Mon, Aug 31, 2009 at 7:40 AM, Jonathan Aquilina<[email protected]> wrote: > One thing that has yet to be mentioned is what kind of gpu are we talking > about. depending on the problem would tesla gpu's, if you are building the > cluster from scratch, be better for a gpu based cluster as they are meant > for high performance computing?
Tesla (10 series) solutions have a big bunch of memory (4GB per GPU) and no graphics card component. In terms of FLOPS the geforce 2xx series are also great (but with less memory per GPU). The tesla C1060 that I work with generates massive heat (they need 200W per card to work), so that is an issue to care about (I have a cold - ~16Celsius - air flow at front of the PC). The 1070 is a 4 GPU "all in one" 1U blade, so I guess it's probably the optimal solution from a management point of view (don't forget the host PC too). The problem with too much data within the GPU (total 16GB) is the bootlenecks (at PCI-x bus) you may have if you need to download big bunches of data frequently or if the code in GPU A is supposed to interact with GPU B/C/D. One thing that's not mentioned out loud by NVIDIA (I have read only in CUDA programming manual) is that if the video system needs more memory that's not available(say you change resolution, while you're waiting for your process to finish), it will crash your cuda app, so I advise you to use a second card to display (if you have a tesla solution, you certainly have a "second" display card). If you are running remotly, this i an non issue (framebuffers don't need much memory neither change resolution). Gil Brandao _______________________________________________ Beowulf mailing list, [email protected] sponsored by Penguin Computing To change your subscription (digest mode or unsubscribe) visit http://www.beowulf.org/mailman/listinfo/beowulf
