Re:How to Make Python Run as Fast as Julia
by lottin on Wednesday December 16, 2015 @04:30AM
(#10743223)
But to be fair, you need to compare the same exact program. A program that handles arbitrary precision isn't the same program as one that doesn't. The same goes for support for missing values and other stuff.
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Re:How to Make Python Run as Fast as Julia
by thebooktocome on Wednesday December 16, 2015 @08:21AM
(#10744046)
The OP was misleading. The benchmark is to calculate fib(20) = 6765, which does not overflow.
You can verify this for yourself at https://github.com/JuliaLang/julia/blob/master/test/perf/mic...
Re:How to Make Python Run as Fast as Julia
by jfpuget on Wednesday December 16, 2015 @12:16PM
(#10745656)
The issue is that the Python code uses arbitrary precision while Julia code uses 64 bits. Python may be slower because of that. One way to get rid of this discrepancy is to compile with typed Cython, or with Numba, which is what I did. The other way would be to benchmark Julia with BigInt. Either way would be fair IMHO.
Re:How to Make Python Run as Fast as Julia
by thebooktocome on Wednesday December 16, 2015 @10:11PM
(#10749242)
I was unaware that Python 3 removed the distinction between int (which used to be C-style int) and long (which was arbitrary precision).
If it's really idiomatic Python 3 to always use arbitrary precision integers for everything, then it's not really Julia's fault that Python 3 makes it more difficult to use performant arithmetic.