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by davrosthedalek 1831 days ago
Ah, I see. That explains a lot. Could also be the reason why it beats gfortran in the test. A JIT knows more about the data the program sees/runtime behavior than a normal compiler. Still surprising. Or is gfortran just so much behind state of the art fortran compilers?
1 comments
eigenspace 1831 days ago
Not this test. Julia’s JIT compiler does not know about runtime values, it’s not a tracing JIT like the ones you may be used to. Instead, it’s ‘just ahead of time’, compiling a specialized executable for each new function signature the first time that signature gets hit. I.e. if I do

    f(x) = 2x + 1
The first time I call

    f(1)
it’ll compile a function specialization for f( ::Int), and the first time you call f on that integer it’ll be slow and all the subsequent calls will be equally fast.

Next if you do

    f(1.0 + 2im)
it’ll compile a new specialization for f(::Complex{Float64}) which will be slow the first time while it compiles and then fast on all the subsequent runs.

The genius of Julia’s design is that the JIT compiler is designed around the semantics of multiple dispatch, and the multiple dispatch semantics are designed around having a JIT

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