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""Gaming" GPU's have reduced compute capabilities" Not so for machine learning, at least in my experience. http://pjreddie.com/darknet/hardware-guide/ http://

by dharma1 10y ago

""Gaming" GPU's have reduced compute capabilities" Not so for machine learning, at least in my experience. http://pjreddie.com/darknet/hardware-guide/ http://pjreddie.com/darknet/hardware-guide/ Passthrough: https://www.evonide.com/non-root-gpu-passthrough-setup/ https://www.evonide.com/non-root-gpu-passthrough-setup/

dogma1138 10y ago
You missed the end of the sentence: "....pretty horrible DP/HP compute performance compared to Tesla parts." Might not be applicable in your case, but NVIDIA hasn't released a desktop part with FP64 capabilities even remotely comparable to their Tesla's since the original Titan. Both Titan X's (Maxwell and Pascal) and the [Insert Physicist's Name Here] Quadros are pretty gimped as far as it comes to HP/DP operations. If anything between generations DP performance across desktop parts have been severely degraded from 3:1 for Kepler to 16:1 for Maxwell to bloody 32:1 for Pascal desktop GPU's. As for the passthrough it is possible but it requires a few hacks and is technically in violations of NVIDIA's policies, and unlike the Tesla parts there is no full virtualization once the a device has been initialized it can only be assigned to 1 host/guest and a full host reboot is required to reinitialize the device again. You also need to do a few UEFI hacks to prevent the UEFI from initializing the GPU before your host OS loads and it can be passed through to the guest. This isn't something that cloud providers would dealing with, NVIDIA will not sell them anything but Tesla/Grid parts and buying from AIB's/OEM's and hacking your way through it isn't an option, you'll get zero support from NVIDIA, you could not thin provision your GPU's and you'll need to do a full host reboot every time you want to reassign the GPU or try to figure out if you can write a custom BIOS for your card that would somehow allow you to reinitialize it. P.S. I've actually built a KVM multi-user gaming rig with NVIDIA cards, I would not recommend anyone to try that, AMD cards are more pleasant to work with, too bad the performance are utter garbage for high end gaming.
- pjietr 10y ago
  Agree with dharma1 that the FP32-based cloud services could be a lot useful for deep learning than the Tesla-based Clouds. Baidu has actually built Titan X -based servers for themselves as they saw it as the most cost-efficient way to build the system: http://www.nextplatform.com/2016/04/22/baidu-eyes-deep-learning-strategy-wake-new-gpu-options/ http://www.nextplatform.com/2016/04/22/baidu-eyes-deep-learn... Same logic would probably work in "deep learning" clouds if there would not be the licensing issue. And this conversation got immortalized to that slideshow as well for selecting hardware parts for deep learning systems: http://tinyurl.com/zzhj9vr http://tinyurl.com/zzhj9vr

by dharma1 10y ago

dogma1138 10y ago

You missed the end of the sentence: "....pretty horrible DP/HP compute performance compared to Tesla parts." Might not be applicable in your case, but NVIDIA hasn't released a desktop part with FP64 capabilities even remotely comparable to their Tesla's since the original Titan. Both Titan X's (Maxwell and Pascal) and the [Insert Physicist's Name Here] Quadros are pretty gimped as far as it comes to HP/DP operations. If anything between generations DP performance across desktop parts have been severely degraded from 3:1 for Kepler to 16:1 for Maxwell to bloody 32:1 for Pascal desktop GPU's. As for the passthrough it is possible but it requires a few hacks and is technically in violations of NVIDIA's policies, and unlike the Tesla parts there is no full virtualization once the a device has been initialized it can only be assigned to 1 host/guest and a full host reboot is required to reinitialize the device again. You also need to do a few UEFI hacks to prevent the UEFI from initializing the GPU before your host OS loads and it can be passed through to the guest. This isn't something that cloud providers would dealing with, NVIDIA will not sell them anything but Tesla/Grid parts and buying from AIB's/OEM's and hacking your way through it isn't an option, you'll get zero support from NVIDIA, you could not thin provision your GPU's and you'll need to do a full host reboot every time you want to reassign the GPU or try to figure out if you can write a custom BIOS for your card that would somehow allow you to reinitialize it. P.S. I've actually built a KVM multi-user gaming rig with NVIDIA cards, I would not recommend anyone to try that, AMD cards are more pleasant to work with, too bad the performance are utter garbage for high end gaming.

pjietr 10y ago
Agree with dharma1 that the FP32-based cloud services could be a lot useful for deep learning than the Tesla-based Clouds. Baidu has actually built Titan X -based servers for themselves as they saw it as the most cost-efficient way to build the system: http://www.nextplatform.com/2016/04/22/baidu-eyes-deep-learning-strategy-wake-new-gpu-options/ http://www.nextplatform.com/2016/04/22/baidu-eyes-deep-learn... Same logic would probably work in "deep learning" clouds if there would not be the licensing issue. And this conversation got immortalized to that slideshow as well for selecting hardware parts for deep learning systems: http://tinyurl.com/zzhj9vr http://tinyurl.com/zzhj9vr