Taichi (太极) is a parallel programming language for high-performance numerical computations. It is embedded in Python, and its just-in-time compiler offloads compute-intensive tasks to multi-core CPUs and massively parallel GPUs.
Advanced features of Taichi include spatially sparse computing, differentiable programming [examples], and quantized computation.
Please check out our SIGGRAPH 2020 course on Taichi basics: YouTube, Bilibili, slides (pdf).
Examples (More...)
python3 -m pip install taichiSupported OS: Windows, Linux, Mac OS X; Python: 3.6-3.9 (64-bit only); Backends: x64 CPUs, CUDA, Apple Metal, Vulkan, OpenGL Compute Shaders.
Please build from source for other configurations (e.g., your CPU is ARM, or you want to try out our experimental C backend).
Note:
- The PyPI package supports x64 CPU, CUDA 10/11, Metal, Vulkan and OpenGL Compute Shader backends.
- On Windows, please install Microsoft Visual C++ Redistributable if you haven't.
- [All releases]
We'd love to hear your comments or any of your feedback! If you would like to contribute to Taichi, please check out the Contribution Guidelines first.
Note: contributor avatars above are randomly shuffled.
If you use Taichi in your research, please cite related papers:
- (SIGGRAPH Asia 2019) Taichi: High-Performance Computation on Sparse Data Structures [Video] [BibTex] [Code]
- (ICLR 2020) DiffTaichi: Differentiable Programming for Physical Simulation [Video] [BibTex] [Code]
- (SIGGRAPH 2021) QuanTaichi: A Compiler for Quantized Simulations [Video] [BibTex] [Code]
- TaichiCon: Taichi developer conferences.
- GAMES 201 Lectures: (Chinese) A hands-on course on building advanced physics engines, based on Taichi.
- TaichiZoo: Running Taichi code in your browser 1.
- 加入太极图形.
- 太极图形课.
- More...
Please disclose security issues responsibly to security@taichi.graphics.
1. TaichiZoo is still in its Beta version. If you've encountered any issue, please do not hesitate to file a bug.