Accelerating Python Code with Taichi: Up to 100× Speed Boosts

Taichi is a Python‑embedded domain‑specific language that compiles @ti.kernel‑decorated functions to run on CPUs and GPUs, providing dramatic speed improvements for compute‑intensive tasks.

Three examples illustrate its impact:

Counting prime numbers below a given N: the original Python code takes 2.235 s for N=1 000 000, while adding import taichi as ti and two decorators reduces the time to 0.363 s (≈120× faster). For N=10 000 000 the speedup reaches about 70×.

Solving the Longest Common Subsequence (LCS) problem via dynamic programming: the Taichi version finishes in 0.9 s, whereas a NumPy implementation needs 476 s (over 500× slower).

Simulating a reaction‑diffusion system: the GPU‑backed Taichi implementation runs at >300 fps, far surpassing the ~30 fps achievable with a Numba version.

All examples require only importing the taichi library and adding a couple of decorators; the core algorithm code remains unchanged.

Installation is straightforward with pip install taichi , and the library works seamlessly with other Python packages such as NumPy, Matplotlib, and PyTorch.

For a complete performance comparison and additional details, refer to the official Taichi blog at https://docs.taichi-lang.org/blog/accelerate-python-code-100x .