ArrayFire
angle
Our great sponsors
ArrayFire | angle | |
---|---|---|
6 | 20 | |
4,404 | 3,237 | |
1.2% | 2.1% | |
7.8 | 9.6 | |
25 days ago | 7 days ago | |
C++ | C++ | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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
-
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.
-
seeking C++ library for neural net inference, with cross platform GPU support
What about Arrayfire. https://github.com/arrayfire/arrayfire
-
[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
-
[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
-
Arrayfire progressive performance decline?
Your Problem may be the lazy evaluation, see this issue: https://github.com/arrayfire/arrayfire/issues/1709
angle
-
Meta releases open source Intermediate Graphics Library which runs on top of Vulkan, Open GL, or Metal on multiple operating systems.
It's using MoltenVK on Mac for Vulkan compatibility, and ANGLE for OpenGL compatibility on Windows and Linux. Makes you wonder what it's actually doing itself.
-
Apple’s Game Porting Toolkit is Wine
Can't they use https://github.com/google/angle?
- can some help me setup OpenGL ES
-
Learn WebGPU
BTW. For the last 15 years, all web browsers on Windows do WebGL on top of DirectX (using the Angle library https://github.com/google/angle).
- Game crashing when downloading additional content on Windows Subsystem for Android
- Exynos 2200, s22u, citra 3ds question
-
Vulkan update: version 1.2 conformance for Raspberry Pi 4
You can still use OpenGL. Just not the vendor provided drivers. They are indeed horrible. There are libraries like:
* ANGLE ( https://github.com/google/angle ) - An OpenGL ES implementation with Direct3D 9, Direct3D 11, Desktop GL, GL ES, Vulkan and Metal backends. This is what we used to use for shipping our Qt 3D application, that used a bunch of OpenGL Shaders. We used to get bug reports about various shaders not working properly on various hardware. After switching to this, all those bug reports vanished.
* Zinc ( https://www.supergoodcode.com/do-not/ ) - A more recent, OpenGL implementation on top of Vulkan. I haven't used this one yet. But they are making a lot of progress and it is almost as performant as vendor provided OpenGL Drivers these days. So if I ever have to ship a desktop app, needing opengl, I'd strongly consider using this.
-
Starting a new Project in 2022, Vulkan or OpenGL?
Another option if perf isn't a big concern is to try using Google's angle OpenGL ES 3.0 implementation, there's backends for it to be supported on Vulkan, Direct3D 11, and (work in progress) Metal. Disclosure, haven't tried it myself, just looking at their feature descriptions: https://github.com/google/angle
-
Minimising input latency in OpenGL
I don't believe Chrome uses OpenGL directly- any OpenGL usage it does have (e.g. for WebGL) is actually going through ANGLE which translates it all to D3D on Windows.
-
Need Help Downloading From GitHub
RTFM, have you read their instruction
What are some alternatives?
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
bgfx - Cross-platform, graphics API agnostic, "Bring Your Own Engine/Framework" style rendering library.
Boost.Compute - A C++ GPU Computing Library for OpenCL
ar_flutter_plugin - Flutter Plugin for AR (Augmented Reality) - Supports ARKit on iOS and ARCore on Android devices
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
OpenGL-on-DXGI - How to use WGL_NV_DX_interop2 to use OpenGL in a DXGI window
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
wgpu-native - Native WebGPU implementation based on wgpu-core
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
model_viewer.dart - A Flutter widget for rendering interactive 3D models in the glTF and GLB formats.
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System
swiftshader - SwiftShader is a high-performance CPU-based implementation of the Vulkan graphics API. Its goal is to provide hardware independence for advanced 3D graphics.