What's the advantage of enroot over charliecloud, which is unprivileged in the sense of being installable in your home directory (given user namespaces)? https://hpc.github.io/charliecloud/
It is the same idea, we actually considered it at first.
There are some differences in the implementation though and we built enroot with the idea of being more extensible.
We also have a plugin for SLURM (https://github.com/NVIDIA/pyxis)
There are several things that can impact performance on "traditional" container runtimes. For example, cgroups, LSMs, seccomp (especially with spectre mitigations), network NS/bridges, etc. There are also more subtle things like being able to do CMA, or deal with shared memory. Most runtimes let you opt out but this becomes difficult to manage and secure with multiple users.
I was originally appalled at the software limiting. But according to Tim Dettmers who has a solid record of predicting and comparing NVIDIA cards for deep learning performance, it's not really a big deal.
Essentially from my understanding it's memory bandwidth which is the real critical path on performance in most cases. The previous generation of Turing cards had more compute than was necessary so they were an underutilized resource.
Also, this Puget benchmark is using an older version of the CUDA drivers. I believe performance is much better in CUDA 11.1.
When I first read the review, I couldn't understand why the author was mentioning that FP16 will surely be improved by new drivers despite not understanding that FP16 TFlops are exactly the same as FP32, tensor cores were nerfed and FP32 accumulate set to 0.5x speed, instead of Titan RTX's 1x. I'd say the results are as good as it gets, if you want a better performance, wait for Ampere Titan or Quadro.
I was as well, until I saw the Linus Tech Tips review of it: the drivers are (were?) missing support for some Titan optimizations -- the Titan RTX significantly beat the 3090 for a few benchmarks.
If the card doesn't have these optimizations, I would expect that an actual 30 series Titan is coming at some point... But the marketing has been really confusing, so who the hell knows.
Not only that they gimped the tensor cores. While it has way more shading units it only has 60% of the tensor cores that the Titan RTX has. I'm not sure how much of a difference this makes in practice, but it leads me to believe this is not the titan level card.
It’s typically problematic to compare cores across generations. They are pretty different in 30xx vs 20xx. Half as many but roughly twice as fast in most tasks.
20xx had 2060 - low end, 2070 - mid, 2080 - high. The 3000 have 3070, 3080 and 3090. It looks to me that 3090 is the equivalent of 2080 (TI or super or what have you), not a step above (name wise).
well ... it was wishful or hopeful thinking :-) I do hope to see better performance but I'm not as optimistic now as I was at first (got some pretty enlightening comments on the post)
Guess that is maximum many of us can afford. Hence features that are missing from A100 is a bit moot. But the update we are wait. Still based on what we saw 3090 really does not worth the money. Still 24gb is 24gb.
If you run it 24/7, anyways the cost of electricity will be much more in a year or so. If you run it sparingly, within some time cloud based 30 series will be launched for sure.
What is the price of electricity where you live? For me to pay as much as the card is worth in electricity, even running it continuously, it would take 11 years.
Granted, electricity is exceedingly cheap here, but still, 11 years is a long time.
A way of creating a pipeline of future customers; goodwill; increased clue and interest in machine learning, which may help them sell more platform services in the future.
https://github.com/NVIDIA/enroot
It basically returns containers to their chroot origins, promising "no performance overhead." I'm looking forward to more posts on that.