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In my (somewhat limited) experience, Numba works great until it doesn't. Loops on numpy arrays work great. Other stuff becomes super slow, without any real way
by cschmidt 10y ago
In my (somewhat limited) experience, Numba works great until it doesn't. Loops on numpy arrays work great. Other stuff becomes super slow, without any real way to understand what is happening. Julia sees more understandable.
- marmaduke 10y agoLol, "works great until it doesn't" applies to any abstraction, including your L3 cache. What's specifically better about Julia here?
- cschmidt 10y agoNumba seems to fall back to regular slow python code when it can't handle things, without any warning. I couldn't find a way to tell if a routine was compiling to fast code, or dropping back to slow code. (As I say, I didn't use it that much, so it might exist.) With Julia is it more well defined when your code is fast. Write type stable code, according to these guidelines... http://docs.julialang.org/en/release-0.4/manual/performance-tips/ http://docs.julialang.org/en/release-0.4/manual/performance-... and you code will be going fast. Specifically, @code_warntype tells you when your code is going to be slow.