Taichi vs. Numba: As its name indicates, Numba is tailored for Numpy. Numba is recommended if your functions involve vectorization of Numpy arrays. Compared wit

Taichi vs. Numba: As its name indicates, Numba is tailored for Numpy. Numba is recommended if your functions involve vectorization of Numpy arrays. Compared with Numba, Taichi enjoys the following advantages: Taichi supports multiple data types, including struct, dataclass, quant, and sparse, and allows you to adjust memory layout flexibly. This feature is extremely desirable when a program handles massive amounts of data. However, Numba only performs best when dealing with dense NumPy arrays. Taichi can call different GPU backends for computation, making large-scale parallel programming (such as particle simulation or rendering) as easy as winking. But it would be hard even to imagine writing a renderer in Numba.