Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

CUDA Toolkit Release Notes

docs.nvidia.com

Post reply on HN
Print view View original

61–70 of 126 posts

Re: CUDA Toolkit Release Notes

#61

Post by [deleted] »

pilooch wrote: post #51
andybak wrote: post #43

Earlier quoted context omitted.

Any title that gives us a clue why the story is worthy of note. At the moment it's frustrating as you have to click through to find out if you want to click through. (title at the time of my comment was "CUDA Toolkit Release Notes")

As an ML + open source developer for over 10y, MacOS support for deep learning is already long gone, Linux is the prime AI/ML OS. However, Apple and NVidia parting away is a good omen for GPU competition I believe. Whatever Apple hw comes up with, if usable outside MacOS software stack, it'd be an interesting alternative.

[deleted]

Re: CUDA Toolkit Release Notes

#62

Post by fluffy87 »

ComodoHacker wrote: post #59
solidasparagus wrote: post #47

Earlier quoted context omitted.

It's extremely hard to use the standard ML toolchains for GPU on anything that doesn't support CUDA.

>standard ML toolchains So Nvidia has achieved their goal and pushed ATI out of ML market?

ATI was never in the AI market. They have been trying, but their efforts are a joke compared tobest their competition is doing.

Re: CUDA Toolkit Release Notes

#63

Post by fluffy87 »

Athas wrote: post #44
xvilka wrote: post #32

This is why CUDA and NVIDIA are a cancer. Such an important piece of infrastructure for both graphics and ML should be open source.

The corresponding AMD infrastructure and most related tools are fully open source. The driver is even upstreamed in the Linux kernel!

Those tools suck.

Re: CUDA Toolkit Release Notes

#64

Post by navaati »

pilooch wrote: post #51
andybak wrote: post #43

Earlier quoted context omitted.

Any title that gives us a clue why the story is worthy of note. At the moment it's frustrating as you have to click through to find out if you want to click through. (title at the time of my comment was "CUDA Toolkit Release Notes")

As an ML + open source developer for over 10y, MacOS support for deep learning is already long gone, Linux is the prime AI/ML OS. However, Apple and NVidia parting away is a good omen for GPU competition I believe. Whatever Apple hw comes up with, if usable outside MacOS software stack, it'd be an interesting alternative.

> if usable outside MacOS software stack

Except, of course, according to Apple’s history regarding the matter, it won’t.

Re: CUDA Toolkit Release Notes

#65

Post by semi-extrinsic »

gambiting wrote: post #41
ActorNightly wrote: post #30

Earlier quoted context omitted.

Why would you do anything compute intensive on laptops that suffer from cooling issues?

Why make such a broad statement at all? There are laptops for nearly every need - yes there are some which will throttle very quickly and are only good for light work, but there are laptops in every size that have decent enough cooling that they can run at max load 24/7 without any issues. As to why someone would do this - can you really not think of a single reason?

I'm not so sure I agree. My old laptop (still in use) is a Dell Precision m4800. It has a quad-core i7, and is so thick and heavy the word "laptop" is kind of a joke. It has massive heatsinks and fairly loud fans. Still it will throttle back after ~90 seconds of "make -j 4" or other heavily parallel jobs.

If you have a laptop that will run at 100% load indefinitely, it's only because the manufacturer has chosen a low power CPU.

Re: CUDA Toolkit Release Notes

#66

Post by mantap »

arvinsim wrote: post #37
snuxoll wrote: post #10

Earlier quoted context omitted.

A well deserved vendetta, Nvidia has screwed Apple over multiple times going back to the PowerPC days. After the defective chipset fiasco one can’t blame them for cutting ties.

In the end, it's the customers who lose

In the end it's apple who loses. Customers can use Linux or Windows, but how is apple going to sell $6000 workstations and $5000 displays without even the option to install aftermarket nvidia cards? OK so Apple doesn't trust nvidia as a supplier but why do they forbid the release of drivers? Total insanity.

Re: CUDA Toolkit Release Notes

#67

Post by nevi-me »

PeterisP wrote: post #57
nevi-me wrote: post #25

Earlier quoted context omitted.

Would this from AMD count? https://rocm.github.io/

It's not supported on Mac, and even on Linux seems to have weird limitations on specific kernel versions. It also supports just a subset of AMD chips, which doesn't seem to include the actual AMD chips of the different Macs that I have available. ROCm would count if it's mature enough so that the setup "just works" on any reasonable environment (in the way that it mostly is so for the major ML platforms on nvidia/CUD…

You make a good point, I forgot that it's not Mac or Windows compatible, and the limited hardware support.

Re: CUDA Toolkit Release Notes

#68

Post by pjmlp »

xvilka wrote: post #32

This is why CUDA and NVIDIA are a cancer. Such an important piece of infrastructure for both graphics and ML should be open source.

Not at all, Khronos just keeps missing the boat that there is a large community that doesn't want to be stuck with C for GPGPU programming.

When they took steps to embrace other languages the boat was already in high sea.

Re: CUDA Toolkit Release Notes

#69

Post by sorenjan »

suyash wrote: post #22

We need a CUDA alternative for non NVIDIA gpu's, specially on a Mac.

I recently looked into GPGPU programming, and it seems to be a bit of a mess, which is surprising seeing how long it's been around and how much it is used. I think a large part of it is due to Nvidia using their strong position to hamper anything other than Cuda.

OpenCL 2.0 was announced in 2013, Nvidia added OpenCL 2.0 "for evaluation purposes" in 2017. I don't think they have final support yet.

OpenCL 2.1 uses the same intermediate language as Vulkan, SPIR-V. Nvidia does not seem to support this.

When OpenCL 2.2 was announced in 2017 Khronos said they were working on converging OpenCL and Vulkan compute, but OpenCL will remain separate. I think this means that they will both use the same SPIR-V backend to run shader code, but I don't know.

Khronos also has SYCL. The latest version, 1.2.1, was released a few days ago and uses OpenCL 1.2 as the backend. I'm guessing it doesn't use a newer version because of Nvidia's poor support for them, but AMD doesn't seem to support it well either.

Then there's a multitude of other libraries/platforms, like POCL, HCC, Acoran, that I don't know anything about but found mentions of. AMD has HIP which can convert Cuda to run on AMD hardware, so maybe Cuda is the best option for AMD as well?

I find it a bit of an embarrassment for the industry that it's this messy and complicated, and I don't see it getting better in the near future. It seems as if the proprietary Cuda is still the best option, and that's a big failure for everyone other than Nvidia. I don't want to use a proprietary language, but I want the code to run well on all the major platforms. I still don't know what to use.

https://www.khronos.org/sycl/

http://portablecl.org/

https://gpuopen.com/compute-product/hcc-heterogeneous-comput...

https://www.codeplay.com/products/acoran/

https://gpuopen.com/compute-product/hip-convert-cuda-to-port...

Re: CUDA Toolkit Release Notes

#70

Post by iguy »

navaati wrote: post #64
pilooch wrote: post #51

Earlier quoted context omitted.

As an ML + open source developer for over 10y, MacOS support for deep learning is already long gone, Linux is the prime AI/ML OS. However, Apple and NVidia parting away is a good omen for GPU competition I believe. Whatever Apple hw comes up with, if usable outside MacOS software stack, it'd be an interesting alternative.

> if usable outside MacOS software stack Except, of course, according to Apple’s history regarding the matter, it won’t.

Unless the swift-for-tensorflow effort cross-fertilises something interesting, I suppose? If you were asked to guess the most likely path by which AMD cards become widely useful, perhaps this would be as good a bet as any.
Post reply on HN