The Taichi Programming Language [Details]

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High-Performance Computation on Sparse Data Structures [Paper] [Video]

Updates

# With GPU support (needs CUDA 10.0)
python3 -m pip install taichi-gpu-nightly==0.0.56 --user

# CPU only. No GPU/CUDA needed
python3 -m pip install taichi-nightly==0.0.55 --user

• (Oct 9, 2019) Compatibility improvements. Added a basic PyTorch interface. [Example].
• (Oct 7, 2019) I have released experimental python 3.6 wheels on Linux (tested on Ubuntu 16.04/18.04) for those who are eager to try without building from source. More stable releases are coming in a few days. To install them:

Notes:

• You still need to clone this repo for demo scripts under examples. You do not need to execute install.py. After installation using pip you can simply go to examples and execute, e.g., python3 mpm.py.
• Make sure you have clang-7. On Ubuntu 18.04 you can install it with sudo apt-get install clang-7. See here for installing clang-7 on Ubuntu 16.04. You will also need g++-7 on Ubuntu16.04. To install:

# Ubuntu 16.04 only
sudo apt-get install -y software-properties-common
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt update
sudo apt install g++-7 -y

• Make sure you clear your legacy Taichi installation (if applicable) by cleaning the environment variables (delete TAICHI_REPO_DIR, and remove legacy taichi from PYTHONPATH) in your .bashrc or .zshrc. Or you can simply do this in your shell to temporarily clear them:

export PYTHONPATH=
export TAICHI_REPO_DIR=

The Taichi Library [Legacy branch]

Taichi is an open-source computer graphics library that aims to provide easy-to-use infrastructures for computer graphics R&D. It's written in C++14 and wrapped friendly with Python.

News

• May 17, 2019: Giga-Voxel SPGrid Topology Optimization Solver is released!
• March 4, 2019: MLS-MPM/CPIC solver is now MIT-licensed!
• August 14, 2018: MLS-MPM/CPIC solver reloaded! It delivers 4-14x performance boost over the previous state of the art on CPUs.

Getting Started