Rust-CUDA VS rust-ndarray

Compare Rust-CUDA vs rust-ndarray and see what are their differences.

Rust-CUDA

Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust. (by Rust-GPU)

rust-ndarray

ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations (by rust-ndarray)
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Rust-CUDA rust-ndarray
37 20
2,852 3,307
3.8% 2.9%
0.0 8.1
5 months ago 5 days ago
Rust Rust
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

Rust-CUDA

Posts with mentions or reviews of Rust-CUDA. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-11.

rust-ndarray

Posts with mentions or reviews of rust-ndarray. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-22.
  • Some Reasons to Avoid Cython
    5 projects | news.ycombinator.com | 22 Sep 2023
    I would love some examples of how to do non-trivial data interop between Rust and Python. My experience is that PyO3/Maturin is excellent when converting between simple datatypes but conversions get difficult when there are non-standard types, e.g. Python Numpy arrays or Rust ndarrays or whatever other custom thing.

    Polars seems to have a good model where it uses the Arrow in memory format, which has implementations in Python and Rust, and makes a lot of the ndarray stuff easier. However, if the Rust libraries are not written with Arrow first, they become quite hard to work with. For example, there are many libraries written with https://github.com/rust-ndarray/ndarray, which is challenging to interop with Numpy.

    (I am not an expert at all, please correct me if my characterizations are wrong!)

  • Helper crate for working with image data of varying type?
    1 project | /r/rust | 29 May 2023
    Thanks for sharing. I read this issue on why ndarray does not have a dynamically typed array: https://github.com/rust-ndarray/ndarray/issues/651
  • What is the most efficient way to study Rust for scientific computing applications?
    1 project | /r/rust | 23 May 2023
    You can get involved with the ndarray project
  • faer 0.8.0 release
    6 projects | /r/rust | 21 Apr 2023
    Sadly Ndarray does look a little abandoned to me: https://github.com/rust-ndarray/ndarray
  • Status and Future of ndarray?
    2 projects | /r/rust | 3 Apr 2023
    The date of the last commit of [ndarray](https://github.com/rust-ndarray/ndarray) lies 6 month in the past while many recent issues are open and untouched.
  • How does explicit unrolling differ from iterating through elements one-by-one? (ndarray example)
    1 project | /r/rust | 13 Jan 2023
    While looking through ndarrays src, I came across a set of functions that explicitly unroll 8 variables on each iteration of a loop, with the comment eightfold unrolled so that floating point can be vectorized (even with strict floating point accuracy semantics). I don't understand why floats would be affected by unrolling, and in general I'm confused as to how explicit unrolling differs from iterating through each element one by one. I assumed this would be a scenario where the compiler would optimize best anyway, which seems to be confirmed (at least in the context of using iter() rather than for) here. Could anyone give a little context into what this, or any explicit unrolling achieves?
  • Announcing Burn: New Deep Learning framework with CPU & GPU support using the newly stabilized GAT feature
    7 projects | /r/rust | 6 Nov 2022
    Burn is different: it is built around the Backend trait which encapsulates tensor primitives. Even the reverse mode automatic differentiation is just a backend that wraps another one using the decorator pattern. The goal is to make it very easy to create optimized backends and support different devices and use cases. For now, there are only 3 backends: NdArray (https://github.com/rust-ndarray/ndarray) for a pure rust solution, Tch (https://github.com/LaurentMazare/tch-rs) for an easy access to CUDA and cuDNN optimized operations and the ADBackendDecorator making any backend differentiable. I am now refactoring the internal backend API to make it as easy as possible to plug in new ones.
  • Pure rust implementation for deep learning models
    3 projects | /r/rust | 9 Oct 2022
    Looks like it's an open request
  • The Illustrated Stable Diffusion
    3 projects | news.ycombinator.com | 4 Oct 2022
    https://github.com/rust-ndarray/ndarray/issues/281

    Answer: you can’t with this crate. I implemented a dynamic n-dim solution myself but it uses views of integer indices that get copied to a new array, which have indexes to another flattened array in order to avoid duplication of possibly massive amounts of n-dimensional data; using the crate alone, copying all the array data would be unavoidable.

    Ultimately I’ve had to make my own axis shifting and windowing mechanisms. But the crate is still a useful lib and continuing effort.

    While I don’t mind getting into the weeds, these kinds of side efforts can really impact context focus so it’s just something to be aware of.

  • Any efficient way of splitting vector?
    2 projects | /r/rust | 12 Sep 2022
    In principle you're trying to convert between columnar and row-based data layouts, something that happens fairly often in data science. I bet there's some hyper-efficient SIMD magic that could be invoked for these slicing operations (and maybe the iterator solution does exactly that). Might be worth taking a look at how the relevant Rust libraries like ndarray do it.

What are some alternatives?

When comparing Rust-CUDA and rust-ndarray you can also consider the following projects:

rust-gpu - 🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧

nalgebra - Linear algebra library for Rust.

wgpu - Cross-platform, safe, pure-rust graphics api.

image - Encoding and decoding images in Rust

CUDA.jl - CUDA programming in Julia.

neuronika - Tensors and dynamic neural networks in pure Rust.

GLSL - GLSL Shading Language Issue Tracker

utah - Dataframe structure and operations in Rust

WeasyPrint - The awesome document factory

linfa - A Rust machine learning framework.

vk-bootstrap - Vulkan Bootstrapping Iibrary

dasp - The fundamentals for Digital Audio Signal Processing. Formerly `sample`.