Re:Julia Computing raises $24M Series A
by SatvikBeri on Monday July 19, 2021 @12:52PM
(#27884759)
There are a few reasons why Julia still tends to be faster than numpy:
* Julia can do loop fusion when broadcasting, while numpy can't, meaning numpy uses a lot more memory during complex operations. (Numba can handle loop fusion, but it's generally much more restrictive.)
* A lot of code in real applications is glue code in Python, which is slow. I've literally found in some applications that <5% of the time was spent in numpy code, despite that being 90% of the code.
That said, if your code is mostly in numba with no pure python glue code (not just numpy), you probably won't see much of a difference.
* Julia can do loop fusion when broadcasting, while numpy can't, meaning numpy uses a lot more memory during complex operations. (Numba can handle loop fusion, but it's generally much more restrictive.)
* A lot of code in real applications is glue code in Python, which is slow. I've literally found in some applications that <5% of the time was spent in numpy code, despite that being 90% of the code.
That said, if your code is mostly in numba with no pure python glue code (not just numpy), you probably won't see much of a difference.