I also have to ask... you're not working with global variables, right? On Thu, May 12, 2016 at 4:05 PM, Ford Ox <[email protected]> wrote:
> Why dont you just post your code here? > > Dne čtvrtek 12. května 2016 15:53:35 UTC+2 Anonymous napsal(a): > >> Yes the algorithm I'm testing this on is fairly polished at this point, >> all variables are within a type and they all have strict type >> declarations. The memory allocations are very low compared to the >> vectorized code, so memory-wise the loops are doing their job, but this >> doesn't translate into speed-ups. >> >> On Thursday, May 12, 2016 at 6:46:32 AM UTC-7, Steven G. Johnson wrote: >>> >>> >>> >>> On Thursday, May 12, 2016 at 8:51:44 AM UTC-4, Miguel Bazdresch wrote: >>>> >>>> honestly I've been testing out different devectorizations of my >>>>> algorithms and I keep getting slower results, not faster, so either I >>>>> really suck at writing for loops or Julia is doing a good job with my >>>>> vectorized code. >>>>> >>>> >>> Make sure your loops are in a function — don't benchmark in global scope >>> (see the performance tips sections of the manual). Try running your >>> function through @code_warntype myfunction(args...) and see if it warns >>> marks any variables as type "ANY" (which indicates a type instability in >>> your code, see the performance tips), >>> >>> Also, if you do "@time myfunc(args...)" and it indicates that you did a >>> huge number of allocations, you could either have a type instability or be >>> allocating new arrays in your inner loops (it is always better to allocate >>> arrays once outside your inner loops and then update them in-place as >>> needed). >>> >>
