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CUDA 11.0

docs.nvidia.com

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Re: CUDA 11.0

#51

Post by cjhanks »

einpoklum wrote: post #32
cjhanks wrote: post #24

Does anyone understand why such minor upgrades resulted in a major version bump? Is this some sort of stability check point? Or some other versioning convention?

Well, I think a new microarchitecture means a major bump. So between that and version bumps to to actual major software features, you get to 11 within 13 years or so. Also, GCC 9.x compatibility may seem minor to some, but is significant for others. I also think there's some C++17 support in kernels - that's something too.

Ooh, I missed those. Support for C++17 is pretty major. Thanks. Perhaps my memory is fuzzy, I just remember the CUDA 9->10 switch having some significant (but not major) performance and feature changes.

Re: CUDA 11.0

#52

Post by llukas »

lostmsu wrote: post #48
llukas wrote: post #36

Earlier quoted context omitted.

Do you mean removing Pascal support deprecating Maxwell? How long you'd need it to be supported to be satisfied? Even with OSS projects discussions about ending support are not easy.

I've got confused for a sec on "removing Pascal support", as some of the 10XX GPUs are only 2 years old. Looks like Pascal stays, and Maxwell is removed indeed (it was deprecated in 10.2).

s/Pascal/Kepler/ sorry.

Re: CUDA 11.0

#53

Post by fomine3 »

muska3 wrote: post #39

I'd like to play with CUDA, but I just got a new laptop without an Nvidia GPU, coming from one that had a built in Nvidia GPU. It's got a thunderbolt port, but unfortunately most of the gpu's are quite expensive at around 400$. Does anyone know any cheaper options?

Just ssh to older laptop?

Re: CUDA 11.0

#54

Post by cosmie »

muska3 wrote: post #39

I'd like to play with CUDA, but I just got a new laptop without an Nvidia GPU, coming from one that had a built in Nvidia GPU. It's got a thunderbolt port, but unfortunately most of the gpu's are quite expensive at around 400$. Does anyone know any cheaper options?

For tinkering around, just use Googe Colab[1]. They offer free hosted Jupyter notebooks and have both Nvidia GPU[2] and Google TPU[3] runtime options available.

Here[4] is a notebook that shows how to install CUDA into an environment using the GPU accelerated runtime.

Only major downside is that resources aren't guaranteed (see first section under "Resource Limits" here[5]), so you sporadically may not be able to start a GPU-accelerated runtime session. But that shouldn't be much of a blocker for tinkering purposes.

[1] https://colab.research.google.com/notebooks/intro.ipynb

[2] https://colab.research.google.com/notebooks/gpu.ipynb

[3] https://colab.research.google.com/notebooks/tpu.ipynb

[4] https://colab.research.google.com/github/ShimaaElabd/CUDA-GP...

[5] https://research.google.com/colaboratory/faq.html

Re: CUDA 11.0

#55

Post by fock »

usmannk wrote: post #3

I noticed CUDA 11.0 was almost ready for release last week when I went to install CUDA and the default download page linked to the 11.0 Release Candidate. The 10.1 and 10.2 links were buried behind a link off to the side labeled "legacy". The thing is, no library you use is going to be supporting the CUDA 11.0 RC, that's ridiculous. For example, Pytorch stable is on 10.2 and Tensorflow only goes up to 10.1. This is g…

speaking of poorly organized: did they fix their embedded dependencies yet for glibc 2.30 in actual tagged releases of tensorflow?

Re: CUDA 11.0

#56

Post by fluffything »

infairverona wrote: post #14

Everytime I have to deal with multiple versions of CUDA on Linux I feel like poking my eyes out. I get that supporting developer libraries that have to interact with hardware is hard but come on...

I don't even install them, just use containers.

docker and nvidia-docker work fine for me

Re: CUDA 11.0

#57

Post by fluffything »

muska3 wrote: post #39

I'd like to play with CUDA, but I just got a new laptop without an Nvidia GPU, coming from one that had a built in Nvidia GPU. It's got a thunderbolt port, but unfortunately most of the gpu's are quite expensive at around 400$. Does anyone know any cheaper options?

Google's Compute Cloud. You can play with a powerful GPU for 0.20$/hour, and you get 300$/year free I think.

Its quite easy to set up as well, basically a workstation that you can just connect to with remote desktop, but migrate the hardware it runs on.

Re: CUDA 11.0

#58

Post by pjc50 »

jjoonathan wrote: post #12
usmannk wrote: post #3

I noticed CUDA 11.0 was almost ready for release last week when I went to install CUDA and the default download page linked to the 11.0 Release Candidate. The 10.1 and 10.2 links were buried behind a link off to the side labeled "legacy". The thing is, no library you use is going to be supporting the CUDA 11.0 RC, that's ridiculous. For example, Pytorch stable is on 10.2 and Tensorflow only goes up to 10.1. This is g…

Yeah, and the CUDA 10.0 official Visual Studio demo project build was broken for... looks like a year, at least, because they didn't want to populate the toolkit path. NVidia, you're better than this. https://forums.developer.nvidia.com/t/the-cuda-toolkit-v10-0... > The Conda dependency manager has made this a lot easier Yeah but conda is "Let's do dependency management with a SAT solver, it'll be great!" On a good d…

> "Let's do dependency management with a SAT solver, it'll be great!"

Debian managed something like this over 20 years ago in dpkg. But somehow people must keep reinventing the wheel.

Re: CUDA 11.0

#59

Post by fluffything »

lihan wrote: post #41

Is there Mac OS support?

No, you need to use Metal, or Vulkan + Molten VR, both of which suck for compute, but you don't have that much compute available on Apple hardware anyways, so it shouldn't matter that much.
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