Groups   138 of 99+      julia-users › why's my julia code running slower than matlab, despite performance tips 26 posts by 9 authors      feza    May 8   I have read the performance section and believe I have followed all the suggested guidelines  The same matlab script takes less than 3 seconds, julia 0.45  9.7 seconds   julia 0.5 is even worse...    script src  https:  gist.github.com musmo 27436a340b41c01d51d557a655276783.js    script       feza    May 8   https:  gist.github.com musmo 27436a340b41c01d51d557a655276783      michae... gmail.com    May 8   I see that c is a constant array of Ints, and its elements multiply ux, uy and uz in a loop, where ux, uy and uz are arrays of floats, so there's a type stability problem.      feza    May 8   Good catch altough this still doesn't explain away the difference   code_warntype shows me feq, f, \rho, ux, uy, uz are   red for some reason eventhough I have explictly stated their types...      STAR0SS    May 8   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 .     Patrick Kofod Mogensen    May 8   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 .     Patrick Kofod Mogensen    May 8   For what it's worth  it run in about 3-4 seconds on my computer on latest v0.4.   CPU : Intel Rlang  Core TM  i7-4600U CPU   2.10GHz      feza    May 8   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....      Tim Holy    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  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       feza    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  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      feza    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  Milan  Script is here: https:  gist.github.com musmo 27436a340b41c01d51d557a655276783    This message has been deleted.    STAR0SS    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  Try changing the order of your loops:  for i in 1:nx, j in 1:ny, k in 1:nz  -    inbounds for k in 1:nz, j in 1:ny, i in 1:nx    inbounds disable bounds checking for arrays, it usually makes a small improvement .   This message has been deleted.    feza    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  Wow thank you guys I totally thought  for i in 1:nx, j in 1:ny, k in 1:nz  ran the i index first and then j and then k  !!!!!  This has been a great learning experience.  Much appreciated, now the julia code is about twice as fast!   On Sunday, May 8, 2016 at 1:12:30 PM UTC-4, Tk wrote: Also try: julia -O --check-bounds no yourcode-jl      David Gold    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  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?      feza    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  Well first problem was that the vectorized version of my code was very slow. Then I devectorized still slow, because of the index clashing with the column-major storage I assumed for i  1:10,j 1:10,k 1:10  does the index i first then j then k wrongly...      feza    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  With all that  done, the julia code runs about the same if not better than matlab  using 4 threads       Patrick Kofod Mogensen    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  out of curiosity, what about v0.5?     feza    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  roughly the same speed.   On Sunday, May 8, 2016 at 2:44:19 PM UTC-4, Patrick Kofod Mogensen wrote: out of curiosity, what about v0.5?     Patrick Kofod Mogensen    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  Same as v0.4, or same as before you changed the code?      feza    May 8  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips  I mean the revised script runs just as fast if not a tad faster with the latest master as it does on 0.4.5 :        Christian Peel    May 9  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips    The usual solution is to devectorized your code and to use loops  except for matrix multiplication if you have large matrices .  I am hopeful that ParallelAccelerator-jl  1  2  or similar projects can enable fast vectorized Julia code   1  https:  github.com IntelLabs ParallelAccelerator-jl  2  http:  julialang.org blog 2016 03 parallelaccelerator  --  chris... ieee.org     Ford O.    May 9  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips Other recipients: chris... ieee.org  I have checked the link and read the article. Am I right that the parallel accelerator basically uses Clanguage code instead of julia to do the computation? That would be kinda shame dont you think?  Dne pondělí 9. května 2016 7:00:38 UTC+2 Christian Peel napsal a :      Yichao Yu    May 9  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips Other recipients: chris... ieee.org  On Mon, May 9, 2016 at 1:15 AM, Ford Ox  ford... gmail.com  wrote:    I have checked the link and read the article. Am I right that the parallel    accelerator basically uses Clanguage code instead of julia to do the computation?    That would be kinda shame dont you think?   No I don't think so.   IIUC it uses Clanguage for the threading API, it even has a backend using the  julia threading API.  And the julia threading API is very incomplete  and experimental .  And in general this is not so different from julia generating LLVM IR   especially since LLVM has a Clanguage backend . Generating Clanguage is just usually  not the as efficient as generating LLVM IR since you'll have parser  overhead, much less flexible and expressive, unless, as in this case,  the function API is in Clanguage.       Yichao Yu    May 9  Re:  julia-users  Re: why's my julia code running slower than matlab, despite performance tips Other recipients: chris... ieee.org  On Mon, May 9, 2016 at 2:04 AM, Yichao Yu  yyc... gmail.com  wrote:    On Mon, May 9, 2016 at 1:15 AM, Ford Ox  ford... gmail.com  wrote:     I have checked the link and read the article. Am I right that the parallel     accelerator basically uses Clanguage code instead of julia to do the computation?     That would be kinda shame dont you think?       No I don't think so.       IIUC it uses Clanguage for the threading API, it even has a backend using the    julia threading API.  And the julia threading API is very incomplete    and experimental .    And in general this is not so different from julia generating LLVM IR     especially since LLVM has a Clanguage backend . Generating Clanguage is just usually    not the as efficient as generating LLVM IR since you'll have parser    overhead, much less flexible and expressive, unless, as in this case,    the function API is in Clanguage.   Or in another word, it is at most a shame for LLVM IR for not having a  threading construct  which, admittedly, is a very hard problem but  people are working on it .             Dne pondělí 9. května 2016 7:00:38 UTC+2 Christian Peel napsal a :             The usual solution is to devectorized your code and to use loops  except        for matrix multiplication if you have large matrices .           I am hopeful that ParallelAccelerator-jl  1  2  or similar projects can      enable fast vectorized Julia code            1  https:  github.com IntelLabs ParallelAccelerator-jl       2  http:  julialang.org blog 2016 03 parallelaccelerator           On Sun, May 8, 2016 at 3:37 PM, feza  moham... gmail.com  wrote:             I mean the revised script runs just as fast if not a tad faster with the       latest master as it does on 0.4.5 :                     On Sunday, May 8, 2016 at 5:20:08 PM UTC-4, Patrick Kofod Mogensen wrote:               Same as v0.4, or same as before you changed the code?               On Sunday, May 8, 2016 at 8:55:00 PM UTC+2, feza wrote:                 roughly the same speed.                 On Sunday, May 8, 2016 at 2:44:19 PM UTC-4, Patrick Kofod Mogensen         wrote:                   out of curiosity, what about v0.5?                          --      chris... ieee.org