How Nvidia’s CUDA Monopoly in Machine Learning Is Breaking

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  • HIPIFY

    Discontinued HIPIFY: Convert CUDA to Portable C++ Code [Moved to: https://github.com/ROCm/HIPIFY] (by ROCm-Developer-Tools)

  • From https://news.ycombinator.com/item?id=32904285 re: AMD Rocm, HIPIFY, :

    >> ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :

    >> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.

    > ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :

    >> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.

    > AMD ROcm supports Pytorch, TensorFlow, MlOpen, rocBLAS on NVIDIA and AMD GPUs: https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni...

  • ROCm

    Discontinued AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]

  • > Nothing actually works on non-NVidia hardware. "Actually works" is different from "theoretically works." A lot of what I use theoretically supports ROCm, but in practice, when you try to use it, you have enough bugs big and small, and things crash or don't work right.

    I was surprised when I started playing around with Stable Diffusion a couple of weeks ago and it just kind of worked with ROCm. That being said, I did run into a couple of very annoying issues... almost running out of VRAM seems to trigger GPU timeouts but as far as I can tell the GPU itself is fine and the memory allocation crawled to a halt. This also seems to be much more likely when running at half precision, which is a shame given that the relatively large 16GB VRAM effectively gets cut in half when running at full precision...

    But this is already a big improvement over "ROCm doesn't support RDNA2" [0] which was still the case ~1.5 years ago. I just really hope that AMD keeps up the effort and fixes all the small and big issues that are still around...

    [0]: https://github.com/RadeonOpenCompute/ROCm/issues/1180

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    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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