autograph
rust-gpu
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autograph | rust-gpu | |
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5 | 82 | |
299 | 6,930 | |
- | 1.9% | |
9.2 | 8.2 | |
27 days ago | 8 days ago | |
Rust | Rust | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
autograph
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Where to Learn Vulkan for parallel computation (with references to porting from CUDA)
I'm working on a machine learning library https://github.com/charles-r-earp/autograph implemented in Rust that uses rust-gpu to compile Rust compute shaders to spirv, and then gfx_hal to target metal and dx12. Training performance is currently about 2x slower than pytorch (cuda) on my laptop but I've made significant progress recently and I am targeting 1.5x. While rust-gpu itself has it's own restrictions, it does support inline spirv assembly, which provides direct access to operations not provided in its std lib, thus it's lower level than GLSL. For example, it should be possible to target cuda tensor cores via cooperative matrix operations (I believe Metal supports these as well but this may not be implemented in spirv-cross and certainly isn't in naga). Once I have things a bit more stabilized I'd like to provide more examples, like porting from cuda / opencl, but I'm still figuring out patterns like how to work with 16 and 8 bit types in a nice and portable way.
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autograph v0.1.0
autograph v0.1.0
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What's the current state of GPU compute in rust?
Working on autograph, for machine learning and neural networks. Unlike CUDA / HIP it's threadsafe, but doesn't expose low level things like multiple streams. Most of the shaders are glsl but I'm now using rust_gpu for pure rust gpu code.
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Announcing neuronika 0.1.0, a deep learning framework in Rust
Maybe not for learning but as inspiration I have to plug this amazing effort for ML with (vulkan) shaders: https://github.com/charles-r-earp/autograph
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What do you think about a library that helps reducing the overhead of GPU programming, regarding ndimensional Arrays?
Maybe you'd be interested in checking out my library, https://github.com/charles-r-earp/autograph?
rust-gpu
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Vcc – The Vulkan Clang Compiler
Sounds cool, but this requires yet another language to learn[0]. As someone who only has limited knowledge in this space, could someone tell me how comparable is the compute functionality of rust-gpu[1], where I can just write rust?
[0] https://github.com/Hugobros3/shady#language-syntax
[1] https://github.com/EmbarkStudios/rust-gpu
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Candle: Torch Replacement in Rust
I don't do anything related to data science, but I feel like doing it in Rust would be nice.
You get operator overloading, so you can have ergonomic matrix operations that are typed also. Processing data on the CPU is fast, and crates like https://github.com/EmbarkStudios/rust-gpu make it very ergonomic to leverage the GPU.
I like this library for creating typed coordinate spaces for graphics programming (https://github.com/servo/euclid), I imagine something similar could be done to create refined types for matrices so you don't do matrix multiplication matrices of invalid sizes
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What's the coolest Rust project you've seen that made you go, 'Wow, I didn't know Rust could do that!'?
Do you mean rust-gpu?
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How a Nerdsnipe Led to a Fast Implementation of Game of Life
And https://github.com/EmbarkStudios/rust-gpu/tree/main/examples with the wgpu runner (here it runs the compute shader)
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What is Rust's potential in game development?
I don't know how major they are considered, but Embark Studios is doing quite a bit of Rust in the open source space, most notably (IMO) rust-gpu and kajiya
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[rust-gpu] How do I run/build my own shaders locally?
The examples in the rust-gpu repository are a good place to start
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Posh: Type-Safe Graphics Programming in Rust
There's another project that's similar that's being used by an actual game company: https://github.com/EmbarkStudios/rust-gpu
They see specific advantages here that would outweigh that negative. It's not my space (I play games, but know next to nothing about graphics programming), but there's at least one argument in the other direction.
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Introducing posh: Type-Safe Graphics Programming in Rust
Could this approach work for compute shaders (GPGPU) as well? So far, I think https://github.com/EmbarkStudios/rust-gpu is the state of the art in that area, but it adds a specific Rust compiler backend for generating SPIR-V rather than leaving that up to the driver. That seems more complicated than it needs to be... but maybe it has advantages too? Thoughts?
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Looking for high level GPU computing crate
https://github.com/embarkstudios/rust-gpu Allows you to create shaders (kernals) in Rust.
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With what languages are video games like League of Legends (most likely) programmed?
Also Embark Studios (formers DICE people) is doing a lot of work with Rust, all open source like Rust GPU https://github.com/EmbarkStudios/rust-gpu
What are some alternatives?
neuronika - Tensors and dynamic neural networks in pure Rust.
llama.cpp - LLM inference in C/C++
RustaCUDA - Rusty wrapper for the CUDA Driver API
wgpu - Cross-platform, safe, pure-rust graphics api.
petgraph - Graph data structure library for Rust.
Rust-CUDA - Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.
VkFFT - Vulkan/CUDA/HIP/OpenCL/Level Zero/Metal Fast Fourier Transform library
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
juice - The Hacker's Machine Learning Engine
kompute - General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
ocl - OpenCL for Rust
DiligentEngine - A modern cross-platform low-level graphics library and rendering framework