arrayfire-rust
ArrayFire
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| arrayfire-rust | ArrayFire | |
|---|---|---|
| 4 | 6 | |
| 804 | 4,404 | |
| -0.4% | 1.2% | |
| 0.0 | 7.8 | |
| 7 months ago | 23 days ago | |
| Rust | C++ | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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.
arrayfire-rust
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Making a better Tensorflow thanks to strong typing
Take a look at arrayfire-rust! :)
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Fast Linear Algebra library for Rust
I haven't tried it myself, but I believe that arrayfire-rust supports GPU.
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Saving an ArrayFire Array
the Git Repo is here.
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State of CUDA on Rust in the beginning of 2021 ?
edit: I apparently missed seeing ArrayFire Rust library. It looks like if you wanted to still use Rust and CUDA this might be your best option for at least compute applications.
ArrayFire
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Learn WebGPU
Loads of people have stated why easy GPU interfaces are difficult to create, but we solve many difficult things all the time.
Ultimately I think CPUs are just satisfactory for the vast vast majority of workloads. Servers rarely come with any GPUs to speak of. The ecosystem around GPUs is unattractive. CPUs have SIMD instructions that can help. There are so many reasons not to use GPUs. By the time anyone seriously considers using GPUs they're, in my imagination, typically seriously starved for performance, and looking to control as much of the execution details as possible. GPU programmers don't want an automagic solution.
So I think the demand for easy GPU interfaces is just very weak, and therefore no effort has taken off. The amount of work needed to make it as easy to use as CPUs is massive, and the only reason anyone would even attempt to take this on is to lock you in to expensive hardware (see CUDA).
For a practical suggestion, have you taken a look at https://arrayfire.com/ ? It can run on both CUDA and OpenCL, and it has C++, Rust and Python bindings.
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seeking C++ library for neural net inference, with cross platform GPU support
What about Arrayfire. https://github.com/arrayfire/arrayfire
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[D] Deep Learning Framework for C++.
Low-overhead — not our goal, but Flashlight is on par with or outperforming most other ML/DL frameworks with its ArrayFire reference tensor implementation, especially on nonstandard setups where framework overhead matters
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[D] Neural Networks using a generic GPU framework
Looking for frameworks with Julia + OpenCL I found array fire. It seems quite good, bonus points for rust bindings. I will keep looking for more, Julia completely fell off my radar.
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Arrayfire progressive performance decline?
Your Problem may be the lazy evaluation, see this issue: https://github.com/arrayfire/arrayfire/issues/1709
What are some alternatives?
nalgebra - Linear algebra library for Rust.
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
futhark - :boom::computer::boom: A data-parallel functional programming language
Boost.Compute - A C++ GPU Computing Library for OpenCL
rust-opencl - OpenCL bindings for Rust.
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
ILGPU - ILGPU JIT Compiler for high-performance .Net GPU programs
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
collenchyma - Extendable HPC-Framework for CUDA, OpenCL and common CPU
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
Emu - The write-once-run-anywhere GPGPU library for Rust
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System