This sounds like a very good system. I was not aware of the X10DRG-Q motherboard; usually such mainboards are not available for private customers — this is a great board! I do not know the exact topology of the system compared to the Nvidia Dev box, but if you have two CPUs this means you will have an additional switch between the two PCIe networks and this will be a bottleneck where you have to transfers GPU memory through CPU buffers. This makes algorithms complicated and prone to human error, because you need to be careful how to pass data around in your system, that is, you need to take into account the whole PCIe topology on which network and switch the infiniband card sits etc.
Cuda convnet2 has some 8 GPU code for a similar topology, but I do not think it will work out of the box. If you can live with more complicated algorithms, then this will be a fine system for a GPU cluster. If cpu1 has 40 lanes, then 32 lane for 2 PCI ex 16, 4x for 10Gigabit Lan, 4x for a 4x PCI ex 8x slot shape, which will be cover if you install 3rd graphic card.
The 2nd cpu also provide 32 lane for pci express, then 8x will be 8x slot on the top slot nearest cpu socket.
Pretty complicated. Do you have any infomation how much performnce different, said a single titan x, on a 16x 3.
Yes, that sounds complicated indeed! A 16x 2. I do not think there exists a single solution which is easy and at the same time cheap. In the end I think the training time will not be that much slower if you run 4 GPUs on 8x 3. If you want a less complicated system that is still faster, you can think about getting a cheap InfiniBand FDR card on eBay. First of all, excellent blog! Given your deep learning setup which has 3x GeForce Titan X for computational tasks, what are your monitors plugged in to?
Or is it better to just have another, much cheaper, graphics cards which is just for display purposes? I have my monitors plugged into a single GTX Titan X and I experience no side effects from that other than a couple of hundreds MB memory that is needed for the monitors; the performance for CUDA compute should be almost the same probably something like So no worries here, just plug them in where it works for you on windows, one monitor would also be an option I think. In fact, K20 and TitanX are the same size. I wonder if it is safe for the cooling of the GPU system.
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Hope to have your opinion. A very tiny space between GPUs is typical for non-tesla cards and your cards should be safe. The only problem is, that your GPUs might run slower because they reach their 80 degrees temperature limit earlier. However, this may increase the noise and heat inside the room where your system is located.
Flashing a BIOS for better fan regulation will most and foremost only increase the lifetime of your GPUs, but overall everything should be fine and safe without any modifications even if you operate your cards at maximum temperature for some days without pause I personally used the standard settings for a few years and all my GPUs are still running well. Hi Tim, Thank for your responses. If you have information, please let me know. Indeed, this will work very well if you have only one GPU. I did not know that there was a application which automatically prepares the xorg config to include the cooling settings — this is very helpful, thank you!
I will include that in an update in the future. I just found a way to increase fan speed of multiple GPUs without flashing. Here is my documentation. Which of this 2 configurations would you choose? This is relevant. I do not have experience with Caffe parallelism, so I cannot really say how good it is. So 2 GPUs might be a little bit better than I said in the quora answer linked above.
This will produce a lot of noise and heat, but your GPUs should run slightly below 80 degrees, or at 80 degrees with little performance lost. Water cooling is of course much superior but if you have little experience with it it might be better to just go with an air cooled setup.
I have heard if installed correctly, water cooling is very reliable, so maybe this would be an option when somebody else, how is familiar with water cooling helps you to set it up. In my experience, the chassis does not make such a big difference. It is all about the GPU fans, and getting the heat out quickly which is mostly towards the back and not through the case. I installed extra fans for better airflow within the case, but this only make a difference of degrees. What might help more are extra backplates and small attachable cooling pads for your memory both about degrees. I would try with the W one and if it does not work just send it back.
Will post some benchmarks with the newer cuDNN v3 once its build and all setup. How did your setup turn out? I am also looking to either build a box or find something else ready made if it is appropriate and fits the bill. I was thinking of scaling down the nvidia devbox as well.
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Very expensive. Are they no good? The price seems too good to be true. I have heard that they break down, but I have also heard that the folks at Main Gear are very responsive and helpful. Now we are considering production servers for image tasks. One of them would be classification. Considering the differences between training and runtime runtime handles a single image, forward prop only , we were wondering if it would be more cost effective to run multiple weaker GPUs, as opposed to fewer stronger ones….
We are reasoning that a request queue consisting of single-image tasks could be processed faster on two separate cards, by two separate processes, then on a single card that is twice as fast. What are your thoughts on this?
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I think in the end this is a numbers game. Try to overflow a GTX M and a Titan with images and see how fast they go and compare that with how fast you need to be. Additionally, it might make sense to run the runtime application on CPUs might be cheaper and more scalable to run them on AWS or something and only run the training on GPUs. I think a smart choice will take this into account, and how scalable and usable the solution is. Thanks for your reply. Hi Tim, I have a minor question related to 6-pin and 8-pin power connector. My workstation has one 8-pin cable to TWO 6-pin cable connectors.
Is it possible that we plug into these two 6-pin connectors to power up Titan X which requires 6-pin and 8-pin power connectors? Thank you so much. I think this will depend somewhat on how the PSU is designed, but I think you should be able to power two GTX Titan X with one double 6-pin cable, because the design makes it seem that it was intended for just that. Why would they put two 6-pin connectors on a cable if you cannot use them? I think you can find better information if you look up your PSU and see if there is a documentation, specification or something like that.
Is the difference in gained speed even that large? If you wait about months you can get a new Pascal card which should be at least 12x faster than your GTX Ti. I personally would value getting additional experience now as more important than getting less experience now and faster training in the future — or in other words, I would go for the GTX How exactly would I be restricted by the 4GB of ram?
Would I simply not be able to create a network with as many parameters, or would there be other negative effects compared to the 6GB of the Ti? Yes, thats correct, if your convolutional network has too many parameters it will not fit into your RAM. If you want more details have a look at my answer about this equation on quora.