ArrayFire
futhark
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ArrayFire | futhark | |
---|---|---|
6 | 52 | |
4,404 | 2,291 | |
1.2% | 2.2% | |
7.8 | 9.8 | |
24 days ago | 6 days ago | |
C++ | Haskell | |
BSD 3-clause "New" or "Revised" License | ISC 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
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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.
- Windows 11 va bloquer les bidouilles qui facilitent l'emploi d'un navigateur alternatif à Edge
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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
futhark
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What downsides exist to Futhark? Seems almost too good to be true?
Why Futhark? (futhark-lang.org)
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GPU Programming: When, Why and How?
There is no on-going work to support Metal apart from the work done by Miles. There's an old issue about it: https://github.com/diku-dk/futhark/issues/853#issuecomment-5...
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Is Parallel Programming Hard, and, If So, What Can You Do About It? v2023.06.11a
Functional programming can be a great way to handle parallel programming in a sane way. See the Futhark language [1], for example, that accepts high-level constructs like map and convert them to the appropriate machine code, either on the CPU or the GPU.
[1] https://futhark-lang.org/
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Is there a programming language that will blow my mind?
Futhark - use a functional language to program the gpu
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Does This Language Exist?
You might want to look into Futhark, although it's mainly designed for writing GPU code.
- Learn WebGPU
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Two-tier programming language
Futhark https://futhark-lang.org/
- Best book on writing an optimizing compiler (inlining, types, abstract interpretation)?
- Functional GPU programming: what are alternatives or generalizations of the idea of "number of cycles must be known at compile time"?
- APL: An Array Oriented Programming Language (2018)
What are some alternatives?
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
arrayfire-rust - Rust wrapper for ArrayFire
Boost.Compute - A C++ GPU Computing Library for OpenCL
dex-lang - Research language for array processing in the Haskell/ML family
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
Halide - a language for fast, portable data-parallel computation
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
julia - The Julia Programming Language
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
BQN - An APL-like programming language. Self-hosted!
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System
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.