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This is mentioned directly in the article: Taichi vs. Numba: As its name indicates, Numba is tailored for Numpy. Numba is recommended if your functions involve
by learndeeply 4y ago
This is mentioned directly in the article:
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.
- jack_pp 4y agoExcept some people don't read the article and already assume numpy is "very" optimized that they might gloss over that line without reading much into it. That line also doesn't say that you might get a 10x speed-up while using numba. I remember when I first came across numba I searched HN for references and didn't find many stories or comments praising it so I skipped over it initially so having HN comments might be useful for future HN'ers.