So, the issue here was the indexing clashing up against the column-major storage of multi-dimensional arrays?
On Sunday, May 8, 2016 at 10:10:54 AM UTC-7, Tk wrote: > > Could you try replacing > for i in 1:nx, j in 1:ny, k in 1:nz > to > for k in 1:nz, j in 1:ny, i in 1:nx > because your arrays are defined like a[i,j,k]? > > Another question is, how many cores is your Matlab code using? > > > On Monday, May 9, 2016 at 2:03:58 AM UTC+9, feza wrote: >> >> Milan >> >> Script is here: >> https://gist.github.com/musmo/27436a340b41c01d51d557a655276783 >> >> >> On Sunday, May 8, 2016 at 12:40:44 PM UTC-4, feza wrote: >>> >>> Thanks for the tip (initially I just transllated the matlab verbatim) >>> >>> Now I have made all the changes. In place operations, and direct >>> function calls. >>> Despite these changes. Matlab is 3.6 seconds, new Julia 7.6 seconds >>> TBH the results of this experiment are frustrating, I was hoping Julia >>> was going to provide a huge speedup (on the level of c) >>> >>> Am I still missing anything in the Julia code that is crucial to speed? >>> @code_warntype looks ok sans a few red unions which i don't think are in >>> my control >>> >>> >>> On Sunday, May 8, 2016 at 8:15:25 AM UTC-4, Tim Holy wrote: >>>> >>>> One of the really cool features of julia is that functions are allowed >>>> to have >>>> more than 0 arguments. It's even considered good style, and I highly >>>> recommend >>>> making use of this awesome feature in your code! :-) >>>> >>>> In other words: try passing all variables as arguments to the >>>> functions. Even >>>> though you're wrapping everything in a function, performance-wise >>>> you're >>>> running up against an inference problem >>>> (https://github.com/JuliaLang/julia/issues/15276). In terms of coding >>>> style, >>>> you're still essentially using global variables. Honestly, these make >>>> your >>>> life harder in the end ( >>>> http://c2.com/cgi/wiki?GlobalVariablesAreBad)---it's >>>> not a bad thing that julia provides gentle encouragement to avoid using >>>> them, >>>> and you're losing out on opportunities by trying to sidestep that >>>> encouragement. >>>> >>>> Best, >>>> --Tim >>>> >>>> On Sunday, May 08, 2016 01:38:41 AM feza wrote: >>>> > That's no surprise your CPU is better :) >>>> > >>>> > Regarding devectorization >>>> > for l in 1:q >>>> > for k in 1:nz >>>> > for j in 1:ny >>>> > for i in 1:nx >>>> > u = ux[i,j,k] >>>> > v = uy[i,j,k] >>>> > w = uz[i,j,k] >>>> > >>>> > cu = c[k,1]*u + c[k,2]*v + c[k,3]*w >>>> > u2 = u*u + v*v + w*w >>>> > feq[i,j,k,l] = weights[k]*ρ[i,j,k]*(1 + 3*cu + >>>> 9/2*(cu*cu) >>>> > - 3/2*u2) >>>> > f[i,j,k,l] = f[i,j,k,l]*(1-ω) + ω*feq[i,j,k,l] >>>> > end >>>> > end >>>> > end >>>> > end >>>> > >>>> > Actually makes the code a lot slower.... >>>> > >>>> > On Sunday, May 8, 2016 at 4:37:18 AM UTC-4, Patrick Kofod Mogensen >>>> wrote: >>>> > > For what it's worth it run in about 3-4 seconds on my computer on >>>> latest >>>> > > v0.4. >>>> > > >>>> > > CPU : Intel(R) Core(TM) i7-4600U CPU @ 2.10GHz >>>> > > >>>> > > On Sunday, May 8, 2016 at 10:33:14 AM UTC+2, Patrick Kofod Mogensen >>>> wrote: >>>> > >> As for the v0.5 performance (which is horrible), I think it's the >>>> boxing >>>> > >> issue with closure https://github.com/JuliaLang/julia/issues/15276 >>>> . >>>> > >> Right? >>>> > >> >>>> > >> On Sunday, May 8, 2016 at 10:29:59 AM UTC+2, STAR0SS wrote: >>>> > >>> You are using a lot of vectorized operations and Julia isn't as >>>> good as >>>> > >>> matlab is with those. >>>> > >>> >>>> > >>> The usual solution is to devectorized your code and to use loops >>>> (except >>>> > >>> for matrix multiplication if you have large matrices). >>>> >>>>
