stablehlo
glare-core
| stablehlo | glare-core | |
|---|---|---|
| 5 | 2 | |
| 333 | 4 | |
| 4.2% | - | |
| 9.8 | 9.6 | |
| about 24 hours ago | 2 days ago | |
| MLIR | C | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
stablehlo
-
Nvidia H200 Tensor Core GPU
I am going to paste a cousin comment:
StableHLO[1] is an interesting project that might help AMD here:
> Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE).
From there, their goal would most likely be to work with XLA/OpenXLA teams on XLA[3] and IREE[2] to make RoCM a better backend.
[1] https://github.com/openxla/stablehlo
[2] https://github.com/openxla/iree
[3] https://www.tensorflow.org/xla
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Chrome Ships WebGPU
Also see the recently introduced StableHLO and its serialization format: https://github.com/openxla/stablehlo/blob/main/docs/bytecode...
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OpenXLA Is Available Now
If you mean StableHLO, then it has an MLIR dialect: https://github.com/openxla/stablehlo/blob/main/stablehlo/dia....
In the StableHLO spec, we are talking about this in more abstract terms - "StableHLO opset" - to be able to unambiguously reason about the semantics of StableHLO programs. However, in practice the StableHLO dialect is the primary implementation of the opset at the moment.
I wrote "primary implementation" because e.g. there is also ongoing work on adding StableHLO support to the TFLite flatbuffer schema: https://github.com/tensorflow/tensorflow/blob/master/tensorf.... Having an abstract notion of the StableHLO opset enables us to have a source of truth that all the implementations correspond to.
glare-core
- Fast, simple, hard real time allocator for Rust
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Nvidia H200 Tensor Core GPU
Yes, even ~2012 OpenCL code works incredibly well today for spectral path tracing: https://indigorenderer.com/indigobench
Also my fractal software incl OpenCL multi-GPU / mixed plaftorm rendering: https://chaoticafractals.com/
Both work on [ Nvidia, AMD, Intel, Apple ] x [ CPU, GPU ].
Some of the shared code here: https://github.com/glaretechnologies/glare-core
Don't let anyone tell you OpenCL is dead! Keep writing OpenCL software!!!!1 voice breaks
What are some alternatives?
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
shvulkan - A lightweight and flexible wrapper around the Vulkan API written in C. The library handles part of the boilerplate code expected to be set up by the Vulkan API.
SHA256-WebGPU - Implementation of sha256 in WGSL
mesa - Mesa 3D graphics library (read-only mirror of https://gitlab.freedesktop.org/mesa/mesa/)
wgpu-mm
mlt - MLT Multimedia Framework
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
SHARK - SHARK - High Performance Machine Learning Distribution
mach - zig game engine & graphics toolkit
pygfx - A python render engine running on wgpu.
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. [Moved to: https://github.com/Tracel-AI/burn]