I don’t know for sure why the early pytorch team picked it, but my guess is due to simplicity and performance. NVidia optimizes CUDA better that OpenCL and provides tons of useful performance tuning tools. It is hard to match the CUDA performance with OpenCL even on the same NVidia GPU hardware, and making performant code compatible across different GPU with OpenCL is also hard. I know examples of scientific codes that became simpler and faster (on nvidia hardware) by going from openCL to CUDA but haven’t yet heard of examples the other way around.
Is there any reason OpenCL is not the standard in implementations like PyTorch? Similar performance, open standard, runs everywhere - what's the downside?
How is CUDA-C that much easier than OpenCL? Having ported back and forth myself, the base C-like languages are virtually identical. Just sub "__syncthreads();" for "barrier(CL_MEM_FENCE)" and so on. To me the main problem is that Nvidia hobbles OpenCL on their GPUs by not updating their CL compiler to OpenCL 2.0, so some special features are missing, such as many atomics.
The ease of implementation using CUDA means that your code because effed for life, because it is no longer valid C/C++, unless you totally litter it with #ifdefs to special case for CUDA. In my own proprietary AI inference pipeline I've ended up code-generating to a bunch of different backends (OpenCL SpirV, Metal, CUDA, HLSL, CPU w. OpenMP), giving no special treatment to CUDA, and the resulting code is much cleaner and builds with standard open source toolchains.
Downsides are it can't express a bunch of stuff cuda or openmp can plus the nvidia opencl implementation is worse than their cuda one. So opencl is great if you want a lower performance way of writing a subset of the programs you want to write.
Is the explicit threat of nuclear war by Russia a possible factor here? Ie. Developing nuclear energy would feed into developing nuclear weapons. From what I understand, the anti-war / anti-extinction angle was a primary motivation for at least early Green activists.
Pretty likely this is associated with the huge increase in mental illness associated with isolation during the pandemic. The depression rate tripled from pre-pandemic levels, leading to a 20% increase in suicide rates. Obviously drug use and all the associated risks would increase due to that, but there are also a few studies showing a strong correlation between mental health and life expectancy, even after accounting for drug use.
As mentioned elsewhere, self-reported happiness is notoriously inaccurate, and is not correlated with objective statistics like suicide rate. The suicide rate in 2021 is at an all-time high [1][2]. I think it's more likely that after months of forced self-isolation, people are less willing to show vulnerability and express unhappiness.
An equally valid conclusion is "People are Lazier at Writing Essays When Provided with LLMs".