Zluda: Run CUDA code on Intel GPUs, unmodified

• ZLUDA

  35 7,803 7.0 Rust

  CUDA on AMD GPUs

• libhum

  6 13 7.0 Python

  Libhum is a Python GPU-accelerated library to extract and compare Electricity Frequency Signals (ENF)

Related question, what is the best way to handle kernel compatibility for CUDA, OpenCL, etc ... ?

I had to write a cross-platform kernel a few weeks ago, and I ended using pre-processor guards to make it work with the OpenCL and CUDA compilers [1].

[1] https://github.com/RaphaelJ/libhum/blob/main/libhum/match.ke...

• InfluxDB

  www.influxdata.com featured

  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.

  

• Pytorch

  341 78,436 10.0 Python

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

  

AMD has made several attempts, their most recent effort apparently is the ROCm [0] software platform. There is an official PyTorch distro for Linux that supports ROCm [1] for acceleration. There's also frameworks like tinygrad [2] that (claim) support for all sorts of accelerators. Thats as far as the claims go, I don't know how it handles the real world. If the occasional George Hotz livestream (creator of TinyGrad) is anything to go by, AMD has to rule out a lot of driver issues to be any actual competition for team green.

I really hope AMD manages a comeback like they showed a few years ago with their CPUs. Intel joining the market is certainly helping, but having three big players competing would certainly be desirable for all sorts of applications that require GPUs. AMD cars like the 7900 XTX are already fairly promising on paper with fairly big VRAMs, they'd probably be much more cost effective than NVIDIA cards if software support was anywhere near comparable.

[0]: https://www.amd.com/en/graphics/servers-solutions-rocm

[1]: https://pytorch.org/

[2]: https://github.com/geohot/tinygrad

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