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Top 23 C++ GPU Projects
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DALI
A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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FluidX3D
The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs via OpenCL.
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deepdetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
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CV-CUDA
CV-CUDA™ is an open-source, GPU accelerated library for cloud-scale image processing and computer vision.
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tgfx
A lightweight 2D graphics library for rendering texts, geometries, and images with high-performance APIs that work across various platforms.
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taichi – Productive, portable, and performant GPU programming in Python
Project mention: Does anyone else agree that the links to the latest development version of Open3D don't work? | /r/cscareerquestions | 2023-07-10I 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
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.
If CPU/GPU execution speed is the goal while simultaneously code golfing the source size, https://halide-lang.org/ might have come in handy.
Another option is DALI https://github.com/NVIDIA/DALI For my project while training EfficientNet2, it was a game changer. But it a way harder to implement in code than TorchVision or Kornia.
Project mention: Optimization Techniques for GPU Programming [pdf] | news.ycombinator.com | 2023-08-09I 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.
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.
Project mention: Exploring Open-Source Alternatives to Landing AI for Robust MLOps | dev.to | 2023-12-13For those seeking a lightweight solution for setting up deep learning REST APIs across platforms without the complexity of Kubernetes, Deepdetect is worth considering.
Project mention: An efficient C++17 GPU numerical computing library with Python-like syntax | /r/programming | 2023-10-05
Project mention: Intel Graphics Compute Runtime for OneAPI Level Zero and OpenCL | news.ycombinator.com | 2023-08-02
Project mention: TGFX – A Skia-alternative, lightweight, high-performance 2D graphics library | news.ycombinator.com | 2023-11-07
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).
C++ GPU related posts
- FluidX3D
- Show HN: Flash Attention in ~100 lines of CUDA
- Taichi: Accessible GPU programming, embedded in Python
- Halide v17.0.0
- Earthquake in Japan yesterday may have shifted land 1.3 meters
- Implementing Mario's Stack Blur 15 times in C++ (with tests and benchmarks)
- An efficient C++17 GPU numerical computing library with Python-like syntax
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Index
What are some of the best open-source GPU projects in C++? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | taichi | 24,760 |
| 2 | Open3D | 10,485 |
| 3 | cudf | 7,274 |
| 4 | Halide | 5,700 |
| 5 | meshoptimizer | 4,959 |
| 6 | DALI | 4,914 |
| 7 | MegEngine | 4,713 |
| 8 | cutlass | 4,522 |
| 9 | ArrayFire | 4,404 |
| 10 | cuml | 3,894 |
| 11 | tiny-cuda-nn | 3,379 |
| 12 | FluidX3D | 3,193 |
| 13 | heavydb | 2,902 |
| 14 | deepdetect | 2,493 |
| 15 | CV-CUDA | 2,190 |
| 16 | GLSL-PathTracer | 1,732 |
| 17 | Boost.Compute | 1,497 |
| 18 | rpi-vk-driver | 1,219 |
| 19 | marian | 1,167 |
| 20 | MatX | 1,115 |
| 21 | stdgpu | 1,085 |
| 22 | compute-runtime | 1,063 |
| 23 | tgfx | 1,001 |
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