[Beowulf] GPU Beowulf Clusters

"C. Bergström" cbergstrom at pathscale.com
Sat Jan 30 14:52:43 PST 2010

Jon Forrest wrote:
> On 1/30/2010 4:31 AM, Micha Feigin wrote:
>> It is recommended BTW, that you have at least the same amount of 
>> system memory
>> as GPU memory, so with tesla it is 4GB per GPU.
> I'm not going to get Teslas, for several reasons:
> 1) This is a proof of concept cluster. Spending $1200
> per graphics card means that the GPUs alone, assuming
> 2 GPUs, would cost as much as a whole node with
> 2 consumer-grade cards. (See below)
> 2) We know that the Fermi cards are coming out
> soon. If we were going to spend big bucks
> on GPUs, we'd wait for them. But, our funding
> runs out before the Fermis will be available.
> This is too bad but there's nothing I can do
> about it.
> See below for comments regarding CPUs and cores.
>> You use dedicated systems. Either one 1u pizza box for the CPU and a 
>> matched 1u
>> tesla s1070 pizza box which has 4 tesla GPUs
> Since my first post I've learned about the Supermicro boxes
> that have space for two GPUs
> (http://www.supermicro.com/products/system/1U/6016/SYS-6016GT-TF.cfm?GPU=) 
> .
> This looks like a good way to go for a proof-of-concept cluster. Plus,
> since we have to pay $10/U/month at the Data Center, it's a good
> way to use space.
> The GPU that looks the most promising is the GeForce GTX275.
> (http://www.evga.com/products/moreInfo.asp?pn=017-P3-1175-AR)
> It has 1792MB of RAM and is only ~$300. I realize that there
> are better cards but for this proof-of-concept cluster we
> want to get the best bang for the buck. Later, after we've
> ported our programs, and have some experience optimizing them,
> then we'll consider something better, probably using whatever
> the best Fermi-based card is.
> The research group that will be purchasing this cluster does
> molecular dynamics simulations that usually take 24 hours or more
> to complete using quad-core Xeons. We hope to bring down this
> time substantially.
>> It doesn't have a swap in/swap out mechanism, so the way it may time 
>> share is
>> by alternating kernels as long as there is enough memory. Shouldn't 
>> be done for
>> HPC (same with CPU by the way due to numa/l2 cache and context switching
>> issues).
> Right. So this means 4 cores should be good enough for 2 GPUs.
> I wish somebody made a motherboard that would allow 6-core
> AMD Istanbuls, but they don't. Putting 2 4-cores CPUs on the
> motherboard might not be worth the cost. I'm not sure.
>> The processes will be sharing the pci bus though for communications 
>> so you may
>> prefer to setup the system as 1 job per machine or at least a round 
>> robin
>> scheduler.
> This is another reason not to go crazy with lots of cores.
> They'll be sitting idle most of the time, unless I also
> create queues for normal non-GPU jobs.
>> Take note that the s1070 is ~6k$ so you are talking at most two to three
>> machines here with your budget.
> Ha, ha!! ~$6K should get me two compute nodes, complete
> with graphics cards.
> I appreciate everyone's comments, and I welcome more.
Hi Jon,

I must emphasize what David Mathog said about the importance of the gpu 
programming model.

My perspective (with hopefully not too much opinion added)
OpenCL vs CUDA - OpenCL is 1/10th as popular, lacks in features, more 
tedious to write and in an effort to stay generic loses the potential to 
fully exploit the gpu.  At one point the performance of the drivers from 
Nvidia was not equivalent, but I think that's been fixed.  (This does 
not mean all vendors are unilaterally doing a good job)

HMPP and everything else I'm far too biased to offer my comments 
publicly.  (Feel free to email me offlist if curious)

Have you considered sharing access with another research lab that has 
already purchased something similar?
(Some vendors may also be willing to let you run your codes in exchange 
for feedback.)

I'd not completely disregard the importance of the host processor.

    1) sw thread synchronization chews up processor time
    2) Do you already know if your code has enough computational 
complexity to outweigh the memory access costs?
    3) Do you know if the GTX275 has enough vram?  Your benchmarks will 
suffer if you start going to gart and page faulting
    4) I can tell you 100% that not all gpu are created equally when it 
comes to handling cuda code.  I don't have experience with the GTX275, 
but if you do hit issues I would be curious to hear about them.

Some questions in return..
Is your code currently C, C++ or Fortran?
Is there any interest in optimizations at the compiler level which could 
benefit molecular dynamics simulations?



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