Performance of log.(x)

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Welcome!

My guess would be that you’re getting SIMD operations in numpy but not in Julia. Interestingly, I can’t reproduce your timing numbers; instead, my Julia timing roughly matches yours, but my numpy results are about 10X slower:

julia> x = randn(10000) .+ 5;

julia> @benchmark log.($x)
BenchmarkTools.Trial: 
  memory estimate:  78.20 KiB
  allocs estimate:  2
  --------------
  minimum time:     57.371 μs (0.00% GC)
  median time:      59.739 μs (0.00% GC)
  mean time:        67.735 μs (7.25% GC)
  maximum time:     35.763 ms (99.67% GC)
  --------------
  samples:          10000
  evals/sample:     1

In [3]: x = np.random.randn(10000) + 5

In [4]: %timeit np.log(x)
173 µs ± 5.53 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

There was a related discussion of SIMD optimizations for logs here: Performance of logarithm calculation (when not to use the dot operator?) - #4 by kristoffer.carlsson although I’m afraid I don’t know enough about the details to be sure if it’s still relevant.

You also might want to consider making a separate thread for this discussion, as it’s only tangentially related to the topic of the original post (which was simply a question of syntax, not of performance optimization).

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