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taichi-dev/taichi

★ 28,182C++Audience · developerComplexity · 3/5MaintainedLicenseSetup · hard

TLDR

A Python language that compiles to GPU code automatically, letting you write high-performance parallel code without learning CUDA or low-level GPU programming.

Mindmap

mindmap
  root((Taichi))
    What it does
      Python to GPU compiler
      Automatic parallelization
      CPU fallback support
    Use cases
      Physics simulation
      Computer graphics
      Numerical computing
      AI research
    Tech stack
      Python
      C++ runtime
      CUDA backend
      Vulkan backend
    Getting started
      pip install taichi
      Decorator syntax
      Works cross-platform

Things people build with this

USE CASE 1

Build real-time physics simulations (cloth, fluids, particles) that run fast on GPU without writing CUDA code.

USE CASE 2

Create interactive computer graphics and visual effects by writing parallel pixel-processing code in Python.

USE CASE 3

Speed up numerical computations and matrix operations for AI research by offloading to GPU with simple decorators.

USE CASE 4

Develop robotics simulations that need fast physics calculations while staying in the Python ecosystem.

Tech stack

PythonC++CUDAVulkan

Getting it running

Difficulty · hard Time to first run · 1day+

Requires CUDA/GPU drivers, C++ compiler, and building from source with multiple dependencies.

Apache 2.0 license allows free use for any purpose, including commercial, as long as you include a copy of the license and state any changes made.

In plain English

Taichi Lang lets you write Python code that runs at the kind of speed normally only possible in lower-level languages. Specifically, it lets numerical calculations, the kind that crunch through huge arrays of numbers, run in parallel on the graphics card or across the CPU's cores. Graphics cards are very good at this because they are designed to do many small calculations at the same time, but they are notoriously awkward to program; Taichi tries to give you that speed while letting you keep writing in something that looks like ordinary Python. It works by embedding a small parallel programming language inside Python. You decorate certain functions with markers such as @ti.kernel, and at run time Taichi compiles those functions into efficient machine code for whatever backend you have, using just-in-time compilation. The README lists supported backends including CUDA, Vulkan, OpenGL, Apple Metal, regular x64 and ARM CPUs, and experimental WebAssembly. It also provides hierarchical, multi-dimensional data containers called SNodes that can represent sparse fields, integrates with NumPy and PyTorch, and ships a cross-platform 3D visualiser plus support for differentiable programming and quantised computation. This is useful for real-time physical simulation, numerical computation, visual effects, vision and robotics, or any heavy calculation that would be too slow in plain Python. The README shows installing it with pip and running a small example that paints an animated fractal entirely on the GPU. The implementation under the Python interface is written in C++ and uses JIT compiler frameworks such as LLVM. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
Show me how to write a Taichi kernel that computes a Mandelbrot fractal on the GPU using the @ti.kernel decorator.
Prompt 2
How do I set up Taichi to run on my NVIDIA GPU and what's the simplest example of a parallel loop?
Prompt 3
Write a Taichi program that simulates particle physics (gravity, collisions) and renders it in real-time.
Prompt 4
How do I integrate Taichi GPU code with NumPy arrays and PyTorch tensors in my Python project?
Prompt 5
What's the difference between writing a loop in regular Python vs. wrapping it in @ti.kernel for Taichi?
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