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This is exactly true. Especially on CUDA and the deep learning front. Basically, only option is NVIDIA as the competition has almost nonexistent software framew

by s5ma6n 7y ago

This is exactly true. Especially on CUDA and the deep learning front. Basically, only option is NVIDIA as the competition has almost nonexistent software framework (maybe OpenCL).

deleted 7y ago
[deleted]
zozbot234 7y ago
CUDA is a vendor lock-in scheme. Use OpenCL or Vulkan instead (yes, Vulkan includes support for compute, not just graphics!). AMD supports both, in addition to tools like HIP to help you port legacy CUDA code.
- TomVDB 7y ago
  CUDA is result of being the first mover in an emerging field over a decade ago. It’s a lock in scheme the way Excel is a lock in scheme for Microsoft: a product that provides value which is used to generate revenue. Using Vulkan(?) or OpenCL for deep learning is fine if your goal is helping get out the word that it’s at least possible to do so. It’s not a great idea if you simply want to get your work done with a minimal amount of issues and with up to date support of all the prominent packages. Both are valid objectives. HIP won’t help you one bit if you use one of the many CUDA libraries.
  - zozbot234 7y ago
    "The prominent packages" for deep learning are all open source. If they don't support OpenCL or Vulkan yet, you can work on it and contribute the feature upstream. It's a matter of getting the ball rolling to start with, nothing more than that.
    - TomVDB 7y ago
      cuDNN isn’t open source. > If they don't support OpenCL or Vulkan yet, you can work on it and contribute the feature upstream. It's a matter of getting the ball rolling to start with, nothing more than that. And that’s exactly what I mean with the choice that you have to make. You could do that, and win brownie points with some crowd, but let’s not pretend that doing so wouldn’t have an impact on your work as an AI researcher. “Nothing more than that” is more than a little bit disingenuous when you take into account the actual work that involved, don’t you think? It’s something a company like AMD could (and does?) sponsor by throwing paid employees at it. Or big companies like Alibaba who buy enough HW to trade off HW cost vs R&D spending. But if your goal is to be a deep learning specialist (it’s where the money is today), spending time on these kind of infrastructure issues is a complete waste.

by s5ma6n 7y ago

This is exactly true. Especially on CUDA and the deep learning front. Basically, only option is NVIDIA as the competition has almost nonexistent software framework (maybe OpenCL).

deleted 7y ago

[deleted]

zozbot234 7y ago

CUDA is a vendor lock-in scheme. Use OpenCL or Vulkan instead (yes, Vulkan includes support for compute, not just graphics!). AMD supports both, in addition to tools like HIP to help you port legacy CUDA code.

TomVDB 7y ago
CUDA is result of being the first mover in an emerging field over a decade ago. It’s a lock in scheme the way Excel is a lock in scheme for Microsoft: a product that provides value which is used to generate revenue. Using Vulkan(?) or OpenCL for deep learning is fine if your goal is helping get out the word that it’s at least possible to do so. It’s not a great idea if you simply want to get your work done with a minimal amount of issues and with up to date support of all the prominent packages. Both are valid objectives. HIP won’t help you one bit if you use one of the many CUDA libraries.
- zozbot234 7y ago
  "The prominent packages" for deep learning are all open source. If they don't support OpenCL or Vulkan yet, you can work on it and contribute the feature upstream. It's a matter of getting the ball rolling to start with, nothing more than that.
  - TomVDB 7y ago
    cuDNN isn’t open source. > If they don't support OpenCL or Vulkan yet, you can work on it and contribute the feature upstream. It's a matter of getting the ball rolling to start with, nothing more than that. And that’s exactly what I mean with the choice that you have to make. You could do that, and win brownie points with some crowd, but let’s not pretend that doing so wouldn’t have an impact on your work as an AI researcher. “Nothing more than that” is more than a little bit disingenuous when you take into account the actual work that involved, don’t you think? It’s something a company like AMD could (and does?) sponsor by throwing paid employees at it. Or big companies like Alibaba who buy enough HW to trade off HW cost vs R&D spending. But if your goal is to be a deep learning specialist (it’s where the money is today), spending time on these kind of infrastructure issues is a complete waste.