Numpy.sort vs Julia sort

General Usage Performance
,
5

On a machine with AVX2 I can confirm numpy.sort is faster (but definitely not 10x):

julia> using BenchmarkTools, PyCall

julia> numpy = pyimport("numpy");

julia> a = randn(Float32, 10^8);

julia> apy = pycall(numpy.array, PyObject, a); # convert to npy array

julia> @btime sort($a); @btime pycall($(numpy.sort), PyObject, $apy);
  2.611 s (14 allocations: 850.76 MiB)
  954.375 ms (1 allocation: 16 bytes)

julia> numpy.__version__
"2.1.3"

julia> versioninfo()
Julia Version 1.11.2
Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 32 × AMD Ryzen 9 3950X 16-Core Processor
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver2)
Threads: 1 default, 0 interactive, 1 GC (on 32 virtual cores)

There’s a big difference between numpy v1 and v2 (the latter being much faster). Looking at htop I see only a single core being used all time.

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