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Nvidia Warp: Python framework for high-performance simulation and graphics code

154 points by RafelMri 2y ago

agravier 2y ago
Looks like an alternative to https://www.taichi-lang.org/ https://www.taichi-lang.org/. Would like to see a comparison.
meisel 2y ago
This looks similar to Triton, I wonder what it does differently. But in any case, for any of these libraries, it would be awesome if it could output object files from this, with PTX or SASS code. Then it can be linked into a binary instead of needing a Python environment to run it.
- erwincoumans 2y ago
  Warp outputs its intermediate GPU CUDA or CPU C++ files that can be compiled and linked into a binary. Here is an old example of mine calling Warp kernels from C++: https://github.com/erwincoumans/warp_cpp https://github.com/erwincoumans/warp_cpp
  - meisel 2y ago
    Neat!
- xpe 2y ago
  Triton offers broad GPU support for writing high throughput kernels. Some higher level ML/AI tools, such as PyTorch, can use Triton internally. I don’t know off the top of my head if any simulation libraries do. In what sense do you think they are similar?
adamnemecek 2y ago
Can anyone comment on how this compares with Futhark?
- rowanG077 2y ago
  Not really comparable imo. Futhark is a language. This is just a thin layer over cuda. Which also makes that immediately the worst thing about it.
  - MoonZ 2y ago
    That makes that the best thing, when you're into computing like computational geometry (and not hyped by the AI bubble)
dagmx 2y ago
Previous discussion https://news.ycombinator.com/item?id=40680737 https://news.ycombinator.com/item?id=40680737
dayeye2006 2y ago
Triton is getting a lot of attention for its adoption in PyTorch2 compiler
- almostgotcaught 2y ago
  lol 1) that happened 2 years ago 2) triton was on everyone's radar way before that
openrisk 2y ago
Python and HPC is something receiving quite some attention. The reasoning is that the combination can unlock user productivity,the way it did for data science / machine learning with the use of standardized and (relatively) user-friendly libraries such as numpy/pandas. But the python+hpc end game is not yet visible. There are lots of incompatible frameworks, extensions, compilers etc but nothing sticking out as particularly compeling. Maybe this is the nature of the beast.
- make3 2y ago
  you don't mention the top one, pytorch, which is behind most AI
  - openrisk 2y ago
    pytorch is still rather narrow in the range of applications / tasks it supports (deep learning algorithms and the related workflows). It does illustrate what it takes to address a subdomain comprehensively (supporting different hardware architectures etc.). But not sure it will ever become a general purpose hpc framework for python.
bionhoward 2y ago
I never get past the license, not sure what you can even use this for without breaking 2.7? Any ideas? 2.6 You may not use the Software in any manner that would cause it to become subject to an open source software license; subject to the terms in the “Components Under Other Licenses” section below. 2.7 You may not use the Software for the purpose of developing competing products or technologies or assist a third party in such activities.
- tomalaci 2y ago
  How would 2.7 work if you developed something that didn't compete with NVDA at the time but they later then released a similar product/service? One of many reasons why any form of non-competes are dumb.
- lukan 2y ago
  Not that I am a fan of those licences, but the difference between building a libary and a product using that libary is quite clear? So building a similar libary would be forbidden with this. But making a unrelated scientific simulation is not.
  - johndough 2y ago
    > the difference between building a libary and a product using that libary is quite clear? Where do you see that differentiation? For me, it is not even clear whether the restriction of building competing products only refers to this specific library or to every single product by NVIDIA.
    - lukan 2y ago
      Well, building a framework for high computing sounds like a competing product. Using a high computing framework to build a simulation for science or a game like using it. There will be grey area, but not so grey, that no product can be build with it.
- lucianbr 2y ago
  We're not far from "You may not use this software if NVidia feels like it".
ahrta 2y ago
So, everyone is betting on a language that is barely maintained, is somewhat abandoned by Google and is essentially in the hands of two other corporations. Without a standard. Can we please focus on better C++ libraries for science so we can use any language for wrapping the result?
- frodo8sam 2y ago
  Python? Are you sure? Maybe hardcore hpc types don't use python but it's still the bread and butter of most academics. Especially anyone doing data science/machine learning. If anything it has gotten more popular as R is slowly being displaced.
deleted 2y ago
[deleted]

154 points by RafelMri 2y ago

Looks like an alternative to https://www.taichi-lang.org/ https://www.taichi-lang.org/. Would like to see a comparison.

This looks similar to Triton, I wonder what it does differently. But in any case, for any of these libraries, it would be awesome if it could output object files from this, with PTX or SASS code. Then it can be linked into a binary instead of needing a Python environment to run it.

erwincoumans 2y ago
Warp outputs its intermediate GPU CUDA or CPU C++ files that can be compiled and linked into a binary. Here is an old example of mine calling Warp kernels from C++: https://github.com/erwincoumans/warp_cpp https://github.com/erwincoumans/warp_cpp
- meisel 2y ago
  Neat!
xpe 2y ago
Triton offers broad GPU support for writing high throughput kernels. Some higher level ML/AI tools, such as PyTorch, can use Triton internally. I don’t know off the top of my head if any simulation libraries do. In what sense do you think they are similar?

adamnemecek 2y ago

Can anyone comment on how this compares with Futhark?

rowanG077 2y ago
Not really comparable imo. Futhark is a language. This is just a thin layer over cuda. Which also makes that immediately the worst thing about it.
- MoonZ 2y ago
  That makes that the best thing, when you're into computing like computational geometry (and not hyped by the AI bubble)

dagmx 2y ago

Previous discussion https://news.ycombinator.com/item?id=40680737 https://news.ycombinator.com/item?id=40680737

dayeye2006 2y ago

Triton is getting a lot of attention for its adoption in PyTorch2 compiler

almostgotcaught 2y ago
lol 1) that happened 2 years ago 2) triton was on everyone's radar way before that

openrisk 2y ago

Python and HPC is something receiving quite some attention. The reasoning is that the combination can unlock user productivity,the way it did for data science / machine learning with the use of standardized and (relatively) user-friendly libraries such as numpy/pandas. But the python+hpc end game is not yet visible. There are lots of incompatible frameworks, extensions, compilers etc but nothing sticking out as particularly compeling. Maybe this is the nature of the beast.

make3 2y ago
you don't mention the top one, pytorch, which is behind most AI
- openrisk 2y ago
  pytorch is still rather narrow in the range of applications / tasks it supports (deep learning algorithms and the related workflows). It does illustrate what it takes to address a subdomain comprehensively (supporting different hardware architectures etc.). But not sure it will ever become a general purpose hpc framework for python.

bionhoward 2y ago

I never get past the license, not sure what you can even use this for without breaking 2.7? Any ideas? 2.6 You may not use the Software in any manner that would cause it to become subject to an open source software license; subject to the terms in the “Components Under Other Licenses” section below. 2.7 You may not use the Software for the purpose of developing competing products or technologies or assist a third party in such activities.

tomalaci 2y ago
How would 2.7 work if you developed something that didn't compete with NVDA at the time but they later then released a similar product/service? One of many reasons why any form of non-competes are dumb.
lukan 2y ago
Not that I am a fan of those licences, but the difference between building a libary and a product using that libary is quite clear? So building a similar libary would be forbidden with this. But making a unrelated scientific simulation is not.
- johndough 2y ago
  > the difference between building a libary and a product using that libary is quite clear? Where do you see that differentiation? For me, it is not even clear whether the restriction of building competing products only refers to this specific library or to every single product by NVIDIA.
  - lukan 2y ago
    Well, building a framework for high computing sounds like a competing product. Using a high computing framework to build a simulation for science or a game like using it. There will be grey area, but not so grey, that no product can be build with it.
lucianbr 2y ago
We're not far from "You may not use this software if NVidia feels like it".

ahrta 2y ago

So, everyone is betting on a language that is barely maintained, is somewhat abandoned by Google and is essentially in the hands of two other corporations. Without a standard. Can we please focus on better C++ libraries for science so we can use any language for wrapping the result?

frodo8sam 2y ago
Python? Are you sure? Maybe hardcore hpc types don't use python but it's still the bread and butter of most academics. Especially anyone doing data science/machine learning. If anything it has gotten more popular as R is slowly being displaced.

deleted 2y ago

[deleted]