https://github.com/fatlipp/cuda-tree

CUDA-based Tree builder
https://github.com/fatlipp/cuda-tree

algorithms cpp cuda octree quadtree tree

Last synced: 11 days ago
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CUDA-based Tree builder

• Host: GitHub
• URL: https://github.com/fatlipp/cuda-tree
• Owner: fatlipp
• Created: 2024-03-18T23:09:52.000Z (11 months ago)
• Default Branch: master
• Last Pushed: 2024-05-12T15:02:12.000Z (9 months ago)
• Last Synced: 2024-11-20T11:38:36.710Z (2 months ago)
• Topics: algorithms, cpp, cuda, octree, quadtree, tree
• Language: C++
• Homepage:
• Size: 6.76 MB
• Stars: 3
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## CUDA based Tree builder (QuadTree, Octree)

Octree                                      | QuadTree

:------------------------------------------:|:---------------------------------------------:

![Octree](/assets/Octree1.png) | ![QuadTree](/assets/QuadTree.png)

![Octree](/assets/Skeleton.png) |

Functionality

* CUDA tree builder

* CPU kNN, Radius Search

* 2d, 3d visualization (controls: WSAD R F, Mouse). Additional functionality for QuadTree - click on a Tree for kNN and RadiusSearch

* Random Points Generator

* Configuration file

* An obj file loader

Some implementation details:

[Medium](https://medium.com/@fatlip/cuda-quadtree-octree-72e65216866c)

---

#### Build and Run

```

* mkdir build && cd build

* cmake .. `[-DCUDA_ARCH=SET_YOUR_ARCH]`

* make -j8

* `./cuda_tree_app_TYPE ./app/config/AppConfig.json`

* memory check: `compute-sanitizer --tool memcheck  ./cuda_tree_app_TYPE ./app/config/AppConfig.json`

```

* cuda_tree_app_TYPE:

    * cuda_tree_app_rng - to use random points

    * cuda_tree_app_obj - to read .obj file from `model_path` (only vertices)

#### Config

- `AppConfig.json` - contains general information (points count, pathes to Tree and Render config files)

- `TreeConfig.json`:

  * type - 'Quad' or 'Oct'

  * size - width, height, depth (if Octree)

  * threadsPerBlock - min **128** for QuadTree and **256** for Octree **(1 warp for each leaf)**

- `RenderConfig.json` - all about rendering

#### Requirements

* CUDA 11.8+

* C++ 17+

* OpenGL, GLFW

* nlohmann, tinyobjloader (included)

#### Limitations

* Tested on up to 150 mln points

* Max *stable* depth: QuadTree = 7, Octree = 6

* I recommend to disable rendering (`AppConfig.json`) if more than 50 mln points are used

#### Further development

1. CUDA based kNN, Radius Search

2. Refactoring, CPU, GPU optimization

3. OpenGL interop for faster visualization

4. Raycasting

5. KDTree

---

#### Disclaimer

* Tested on Linux: Ubuntu 22.02, CUDA: 12.2, GPU: 3060 (laptop)

* Approach based on Nvidia's QuadTree sample (cdpQuadtree)