petgraph VS autograph

Are you just trying to throw shade on Rust?

https://doc.rust-lang.org/std/collections/struct.LinkedList....

> NOTE: It is almost always better to use Vec or VecDeque because array-based containers are generally faster, more memory efficient, and make better use of CPU cache.

https://docs.rs/petgraph 78 M downloads

I used to think that since graphs are such a broad datastructure that can be represented in different ways depending on requirements that it just made more sense to implement them at a domain-ish level.

Then I saw Petgraph [0] which is the first time I had really looked at a generic graph library. It's very interesting, but I still have implemented graphs at a domain level.

[0] https://github.com/petgraph/petgraph

For graph algorithms specifically, also consider looking at the implementations in petgraph.

Take a look at: https://github.com/petgraph/petgraph

Render the backward "graph" using petgraph for visualization and debugging purposes.

I second the need for quickcheck-style tests. I implemented a matching algorithm in petgraph, and quickcheck discovered so many bugs on non-trivial graphs. Thanks to it, I am now much more confident that it is indeed correct.

petgraph: Graph data structure library, compatible with Rust

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.

autograph v0.1.0

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.

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

Maybe you'd be interested in checking out my library, https://github.com/charles-r-earp/autograph?