Lint.jl is also good for checking that, depending on how much time you want to spend learning to read the output of code_typed.
On Sat, Sep 13, 2014 at 3:27 PM, Elliot Saba <staticfl...@gmail.com> wrote: > A good way to track down performance issues like this is to use > @code_typed to output the typed code in your function and look for places > where type inference doesn't know what to do; e.g. large type unions, Any > types, etc.... This is often caused by a variable taking on multiple > separate types over its lifetime within the function and can cause > slowdowns inside inner loops. > -E > > On Sat, Sep 13, 2014 at 1:13 PM, Noah Brenowitz <nbre...@gmail.com> wrote: > >> now i am pretty impressed. >> >> On Saturday, September 13, 2014 4:12:07 PM UTC-4, Noah Brenowitz wrote: >>> >>> I just replaced u = u0, with u = complex128(u0) in the julia code. Now >>> it is only 2x as slow as fortran. >>> >> >
