Accelerating Python Code with Taichi: Prime Counting, LCS, and Reaction‑Diffusion Examples

Python is easy to write but can be slow for compute‑intensive tasks; importing the Taichi library can boost speed dramatically.

Example 1: counting primes below a given N. The original Python implementation takes 2.235 s for N=1 000 000, while adding import taichi as ti and two decorators reduces runtime to 0.363 s (≈6× faster). For N=10 000 000 the speedup reaches 70×.

Example 2: longest common subsequence (LCS) solved with dynamic programming. Using Taichi the computation finishes in under 0.9 s, whereas a NumPy implementation needs 476 s (over 500× slower).

Example 3: reaction‑diffusion simulation (Turing patterns). Taichi implements the PDE on a grid with double buffering and GPU acceleration, producing vivid patterns at >300 fps, far exceeding CPU‑only or Numba implementations.

The Taichi DSL compiles @ti.kernel functions to CPU or GPU, allowing high‑performance Python without writing C++/CUDA. Install via pip install taichi and combine with NumPy, Matplotlib, PyTorch, etc.

For more details and a comparison of advantages, see the official documentation.