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Top 23 C++ Cuda Projects
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Project mention: Does anyone else agree that the links to the latest development version of Open3D don't work? | /r/cscareerquestions | 2023-07-10
I was going to file a bug about another issue, but I have to download the development version. This is why I want this solved quickly. None of the links seem to work: https://github.com/isl-org/Open3D/issues/6259
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The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.
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InfluxDB
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Project mention: OneFlow v0.9.0 Came Out!——A Distributed Deep Learning Framework | /r/programming | 2023-02-12
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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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sklearn can't, bit take a look at cuML (https://github.com/rapidsai/cuml ). It uses the same API as sklearn but executes on GPU.
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Project mention: Optimization Techniques for GPU Programming [pdf] | news.ycombinator.com | 2023-08-09
I would recommend the course from Oxford (https://people.maths.ox.ac.uk/gilesm/cuda/). Also explore the tutorial section of cutlass (https://github.com/NVIDIA/cutlass/blob/main/media/docs/cute/...) if you want to learn more about high performance gemm.
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AMD's equivalent is HIP [1], for sufficiently flexible definitions of "equivalent". I can't speak to how complete/correct/performant it is (I'm just a guy running tutorial/toy-level ML stuff on an RDNA1 card), but part of AMD's problem is that it might not practically matter how well they do this because the broader ecosystem support specifically for the CUDA stack is so entrenched.
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Just discovered this artificial life simulation tool and it looks so beautiful.
It's also Open Source: https://github.com/chrxh/alien
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Project mention: [D] Have their been any attempts to create a programming language specifically for machine learning? | /r/MachineLearning | 2023-02-11
In the opposite direction from your question is a very interesting project, TinyNN all implemented as close to the metal as possible and very fast: https://github.com/NVlabs/tiny-cuda-nn
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The original Whisper implementation from OpenAI uses the PyTorch deep learning framework. On the other hand, faster-whisper is implemented using CTranslate2 [1] which is a custom inference engine for Transformer models. So basically it is running the same model but using another backend, which is specifically optimized for inference workloads.
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CV-CUDA
CV-CUDA™ is an open-source, GPU accelerated library for cloud-scale image processing and computer vision.
Project mention: Microsoft, Tencent, Baidu Adopting Nvidia CV-CUDA for Computer Vision AI | news.ycombinator.com | 2023-03-21I'm not familiar with CV-CUDA but it looks interesting.The github may be made useful than the press release: https://github.com/CVCUDA/CV-CUDA
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Project mention: Why Nvidia Keeps Winning: The Rise of an AI Giant | news.ycombinator.com | 2023-07-06
> I don't think you understand just how insanely difficult it is to break into that market.
You're right, I have no clue nor have I ever tried myself.
> Even with apple money or something like that, it's a losing prospect because in the time it'll take you to get up and off the ground (which is FOREVER) your competition will crush you.
This I find hard to believe, do you have a source or reference for that claim? Companies with that amount of cash are hardly going to be crushed by competition be it direct or indirect. Anyway, I'm talking more about the Intels and AMDs of this world.
We have very lacklustre efforts from players I won't name with their Zluda library (https://github.com/vosen/ZLUDA) which I got REALLY excited about, until I read the README.txt. Four contributors, last commit early 2021.
Why, oh why, is it this bad?
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Project mention: [P] - VkFFT now supports quad precision (double-double) FFT computation on GPU | /r/MachineLearning | 2023-09-27
Hello, I am the creator of the VkFFT - GPU Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL/Level Zero and Metal. In the latest update, I have added support for quad-precision double-double emulation for FFT calculation on most modern GPUs. I understand that modern ML is going in the opposite low-precision direction, but I still think that it may be useful to have this functionality at least for some prototyping and development of concepts.
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Project mention: [P] A CLI tool for easy transformer sequence classifier training and inference | /r/MachineLearning | 2023-02-01
As a reference, I forked https://github.com/marian-nmt/marian privately to support sequence tagging tasks. With a positional loss mask, It can also support sequence classificaiton.
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AdaptiveCpp
Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
Project mention: Offloading standard C++ PSTL to Intel, NVIDIA and AMD GPUs with AdaptiveCpp | /r/cpp | 2023-09-24AdaptiveCpp (formerly known as hipSYCL) is an independent, open source, clang-based heterogeneous C++ compiler project. I thought some of you might be interested in knowing that we recently added support to offload standard C++ parallel STL algorithms to GPUs from all major vendors. E.g.:
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For GPU support take a look at our library:
https://github.com/NVIDIA/MatX
If anything is missing we're happy to take feature requests.
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vuda
VUDA is a header-only library based on Vulkan that provides a CUDA Runtime API interface for writing GPU-accelerated applications.
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2023-09-27.
C++ Cuda related posts
- Offloading standard C++ PSTL to Intel, NVIDIA and AMD GPUs with AdaptiveCpp
- Am I not good enough?
- Alien v4.0 (Simulation)
- Optimization Techniques for GPU Programming [pdf]
- Why Nvidia Keeps Winning: The Rise of an AI Giant
- VUDA: A Vulkan Implementation of CUDA
- VUDA: A Vulkan Implementation of CUDA
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Index
What are some of the best open-source Cuda projects in C++? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Open3D | 9,497 |
| 2 | cudf | 5,966 |
| 3 | oneflow | 5,331 |
| 4 | Thrust | 4,773 |
| 5 | ArrayFire | 4,227 |
| 6 | cuml | 3,539 |
| 7 | cutlass | 3,331 |
| 8 | HIP | 3,160 |
| 9 | alien | 3,094 |
| 10 | lightseq | 2,928 |
| 11 | tiny-cuda-nn | 2,881 |
| 12 | heavydb | 2,831 |
| 13 | libcudacxx | 2,287 |
| 14 | CTranslate2 | 1,914 |
| 15 | CV-CUDA | 1,855 |
| 16 | ZLUDA | 1,470 |
| 17 | VkFFT | 1,340 |
| 18 | slang | 1,236 |
| 19 | marian | 1,079 |
| 20 | stdgpu | 994 |
| 21 | AdaptiveCpp | 817 |
| 22 | MatX | 785 |
| 23 | vuda | 772 |
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