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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).

中文视频教程: [哔哩哔哩], [幻灯片]

python3 -m pip install taichi

Supported 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 [email protected].

1. TaichiZoo is still in its Beta version. If you've encountered any issue, please do not hesitate to file a bug.