Groups   59 of 99+      julia-users › Performance compared to Matlab 20 posts by 15 authors      Vishnu Raj    10 18 15   Although Julia homepage shows using Julia over Matlab gains more in performance, my experience is quite opposite. I was trying to simulate channel evolution using Jakes Model for wireless communication system.  Matlab code is: function   h, tf     Jakes_Flat  fd, Ts, Ns, t0, E0, phi_N    JAKES_FLAT      Inputs:         fd, Ts, Ns  : Doppler frequency, sampling time, number of samples         t0, E0      : initial time, channel power         phi_N       : initial phase of the maximum Doppler frequeny         sinusoid       Outputs:         h, tf       : complex fading vector, current time      if nargin   6,  phi_N   0;  end     if nargin   5,  E0   1;     end     if nargin   4,  t0   0;     end          N0   8;           As suggested by Jakes     N    4 N0 + 2;    an accurate approximation     wd   2 pi fd;     Maximum Doppler frequency rad      t    t0 +  0:Ns-1  Ts;    Time vector     tf   t end  + Ts;         Final time     coswt     sqrt 2  cos wd t ; 2 cos wd cos 2 pi N  1:N0 '  t   ;     h    E0 sqrt 2 N0+1  exp j  phi_N pi  N0+1   1:N0    coswt; end Enter code here...  My call results in :    tic; Jakes_Flat  926, 1E-6, 50000, 0, 1, 0  ; toc Elapsed time is 0.008357 seconds.   My corresponding Julia code is function Jakes_Flat  fd, Ts, Ns, t0   0, E0   1, phi_N   0     Inputs:     Outputs:   N0    8;                    As suggested by Jakes   N     4 N0+2;               An accurate approximation   wd    2 pi fd;              Maximum Doppler frequency   t     t0 +  0:Ns-1  Ts;   tf    t end  + Ts;   coswt     sqrt 2  cos wd t' ; 2 cos wd cos 2 pi N  1:N0   t'      h   E0 sqrt 2 N0+1  exp im   phi_N pi  N0+1   1:N0 '     coswt   return h, tf; end   Saved this as  jakes_model-jl    My first call results in  julia  include   jakes_model-jl    Jakes_Flat  generic function with 4 methods   julia   time Jakes_Flat  926, 1e-6, 50000, 0, 1, 0   elapsed time: 0.65922234 seconds  61018916 bytes allocated   julia   time Jakes_Flat  926, 1e-6, 50000, 0, 1, 0   elapsed time: 0.042468906 seconds  17204712 bytes allocated, 63.06  gc time   For first execution, Julia is taking huge amount of time. On second call, even though Julia take considerably less 0.042468906 sec  than first 0.65922234 sec , it's still much higher to Matlab 0.008357 sec . I'm using Matlab R2014b and Julia v0.3.10 on Mac OSX10.10.  - vish     Jason Merrill    10 18 15   Two quick pieces of advice:  Try   code_warntype Jakes_Flat  926, 1e-6, 50000, 0, 1, 0    For some reason, the type of the variable  h  is not being inferred tightly  i.e. it has type any . If you can sort things out so that h is typed more concretely, I suspect you'll be able to match matlab's performance.  However, if you're going for speed, this code is begging to be devectorized. It creates tons of fairly large intermediate arrays. In Julia, don't be afraid of for loops.  It may actually be easier to devectorize the code than it will be to find and fix the typing issue in the original, because the original uses some fairly complicated array concatenation.     Andras Niedermayer    10 18 15   There is a type instability  see here  that slows down your code.    code_warntype Jakes_Flat  926, 1e-6, 50000, 0, 1, 0   shows that variable h has type Any.  I managed to track down the type instability to the concatenation    phi_N pi  N0+1   1:N0 '   You can get around the type instability with this modification: function Jakes_Flat  fd, Ts, Ns, t0   0, E0   1, phi_N   0     Inputs:     Outputs:   N0    8;                    As suggested by Jakes   N     4 N0+2;               An accurate approximation   wd    2 pi fd;              Maximum Doppler frequency   t     t0 +  0:Ns-1;  Ts;   tf    t end  + Ts;   coswt     sqrt 2  cos wd t' ; 2 cos wd cos 2 pi N  1:N0;   t'       temp   zeros 1,N0+1    temp 1,2:end    pi  N0+1   1:N0; '   temp 1,1    phi_N   h   E0 sqrt 2 N0+1  exp im temp     coswt   return h, tf; end  This is faster 30  faster than before, but still not as fast as Matlab. Maybe someone else knows the reason for the speed difference  I guess it's due to temporary arrays .      Andras Niedermayer    10 18 15   The type instability looks like a bug in Julia to me, filed issue: https:  github.com JuliaLang julia issues 13665      Daniel Carrera    10 18 15   Hello,  Other people have already given advice on how to speed up the code. I just want to comment that Julia really is faster than Matlab, but the way that you make code faster in Julia is almost the opposite of how you do it in Matlab. Specifically, in Matlab the advice is that if you want the code to be fast, you need to eliminate every loop you can and write vectorized code instead. This is because Matlab loops are slow. But Julia loops are fast, and vectorized code creates a lot of overhead in the form of temporary variables, garbage collection, and extra loops. So in Julia you optimize code by putting everything into loops. The upshot is that if you take a Matlab-optimized program and just do a direct line-by-line conversion to Julia, the Julia version can easily be slower. But by the same token, if you took a Julia-optimized program and converted it line-by-line to Matlab, the Matlab version would be ridiculously slow.  Oh, and in Julia you also care about types. If the compiler can infer correctly the types of your variables it will write more optimal code.  Cheers, Daniel.      Kristoffer Carlsson    10 18 15   Worth saying is that Julia is not faster than Matlab if most of your time is spent on doing cosinus on a large array, which this is doing.      Glen O    10 19 15   How many cores does your computer have?  Recent versions of Matlab automatically parallelise calculations like the coswt calculations, I believe, whereas Julia requires explicit request for parallel computations. A quick profiling in Julia reveals that it's actually the coswt matrix construction that is taking the vast majority of the time.   On Sunday, 18 October 2015 21:17:50 UTC+10, Vishnu Raj wrote: Although Julia homepage shows using Julia over Matlab gains more in performance, my experience is quite opposite. I was trying to simulate channel evolution using Jakes Model for wireless communication system.     Phil Tomson    10 19 15   Several comments here about the need to de-vectorize code and use for-loops instead. However, vectorized code is a lot more compact and generally easier to read than lots of for-loops. I seem to recall that there was discussion in the past about speeding up vectorized code in Julia so that it could be on par with the vectorized code performance - is this still something being worked on for future versions?  Otherwise, if we keep telling people that they need to convert their code to use for-loops, I think Julia isn't going to seem very compelling for people looking for alternatives to Matlab, R, etc.      Milan Bouchet-Valat    10 19 15  Re:  julia-users  Re: Performance compared to Matlab  Le lundi 19 octobre 2015 à 12:05 -0700, Phil Tomson a écrit :    Several comments here about the need to de-vectorize code and use for    -loops instead. However, vectorized code is a lot more compact and    generally easier to read than lots of for-loops. I seem to recall    that there was discussion in the past about speeding up vectorized    code in Julia so that it could be on par with the vectorized code    performance - is this still something being worked on for future    versions?       Otherwise, if we keep telling people that they need to convert their    code to use for-loops, I think Julia isn't going to seem very    compelling for people looking for alternatives to Matlab, R, etc.  There's a long discussion, with still no perfect solution :  https:  github.com JuliaLang julia issues 8450   But I'm confident something will finally be done about this. :-     Regards       David Gold    10 19 15   One doesn't always need to write the loops oneself. Oftentimes switching from a pure operator  e.g. broadcast  to its in-place counterpart  e.g. broadcast!  can make a world of difference:  julia  A   rand 5_000_000 ;   julia  function f A       sum A .+ A. 2   end  f  generic function with 1 method    julia  f A ;  time f A   0.182246 seconds  206 allocations: 76.307 MB, 5.70  gc time   4.166856979458311e6   julia  _g x, y    x + y 2 _g  generic function with 1 method   julia  function g A       temp   Array Float64,5_000_000       broadcast! _g, temp, A, A       sum temp   end  g  generic function with 1 method   julia  g A ;  time g A   0.023660 seconds  8 allocations: 38.147 MB, 28.73  gc time   4.166856979458311e6  Thus it may help to cultivate knowledgeable use of map! and broadcast! as opposed to pure broadcasted operators amongst newcomers to Julia.      Steven G. Johnson    10 19 15     On Monday, October 19, 2015 at 3:05:11 PM UTC-4, Phil Tomson wrote: Otherwise, if we keep telling people that they need to convert their code to use for-loops, I think Julia isn't going to seem very compelling for people looking for alternatives to Matlab, R, etc.  You only need loops if you are optimizing critical code, and that is usually only a small percentage of most people's code.  It's not like vectorized code is terrible  much worse than Matlab or Rlang , just that it is often not as fast as C; this is also true in Matlab or Rlang — a whole sequence of vectorized operations like x + 2 y. 2 + z. 3 .   x -  5  is not nearly as fast as fusing all the operations into a single Clanguage  or Julia  loop.    The exception, of course, is when Matlab vectorized operations are multi-threaded  as in the original post in this thread  then they can of course be faster than a non-parallel Julia  or Clanguage  vectorized operation or explicit loop.        Daniel Carrera    10 19 15  Re:  julia-users  Re: Performance compared to Matlab  Julia vectorized code should certainly be as fast as Matlab and NumPy vectorized code, and to the best of my knowledge, it already is. But de-vectorized code will always have an advantage because you have low-level control, and you can avoid temp variables.  Cheers, Daniel.      John Pearson    10 19 15   This can be true, but I think a lot of us forget just how much time it takes to learn to write compact vectorized code, and how difficult it can be to vectorize absolutely everything. For instance, it's great to be able to write   x'   A   x  in Matlab, but a whole ton of code using repmat or bsxfun often becomes easier to read  IMO  when you can actually look at a single line in a loop and understand a  how many indices everything has and b  what the  i, j, k, ...  entry of the resulting array looks like.  I have been forced to write a lot of very difficult-to-parse code in Matlab simply in order to avoid what would have been straightforward loops.  On Monday, October 19, 2015 at 3:05:11 PM UTC-4, Phil Tomson wrote:      Gabriel Gellner    10 21 15   Is it possible to tell Julia to run the vectorized code in parallel? Looking at the documentation I see that you can do it easily for the looped version but is there a macro or something to pass the vectorized version so that is becomes parallel?      Stefan Karpinski    10 21 15  Re:  julia-users  Re: Performance compared to Matlab  On Tue, Oct 20, 2015 at 1:07 PM, Gabriel Gellner  gabriel... gmail.com  wrote: Is it possible to tell Julia to run the vectorized code in parallel? Looking at the documentation I see that you can do it easily for the looped version but is there a macro or something to pass the vectorized version so that is becomes parallel?  Not yet but once we introduce threads to the language, it will be.    This message has been deleted.    Kristoffer Carlsson    10 21 15   For fun  and science  I tried out the new package https:  github.com IntelLabs ParallelAccelerator-jl for this problem.  Here is the code:  function Jakes_Flat  fd, Ts, Ns, t0   0, E0   1, phi_N   0     Inputs:     Outputs:   N0    8;                    As suggested by Jakes   N     4 N0+2;               An accurate approximation   wd    2 pi fd;              Maximum Doppler frequency      ts     collect t0 +  0:Ns-1  Ts    tf    ts end  + Ts;   Ns   collect 1:N0     coswt     cosvec ts, wd '; cosmat ts, Ns, wd, N      h   E0 sqrt 2 N0+1  exp im   phi_N pi  N0+1   1:N0 '     coswt   return h, tf; end   acc function cosvec ts, wd      Float64 sqrt 2  cos wd t  for t in ts  end   acc function cosmat ts, Ns, wd, N      Float64 2 cos wd cos 2 pi N n  t  for n in Ns, t in ts  end   Benchmarking this I get:  julia   time Jakes_Flat  926, 1e-6, 50000, 0, 1, 0     0.004779 seconds  115 allocations: 4.965 MB   and without calling the accelerated functions  by putting  noacc in front of the function calls, I get :  julia   time Jakes_Flat_noacc  926, 1e-6, 50000, 0, 1, 0     0.019072 seconds  75 allocations: 8.396 MB   The matlab code on my computer runs at:     tic; Jakes_Flat  926, 1E-6, 50000, 0, 1, 0  ; toc Elapsed time is 0.009936 seconds.  So.. great victory for ParallelAccelerator-jl?  On Sunday, October 18, 2015 at 1:17:50 PM UTC+2, Vishnu Raj wrote:      Kristoffer Carlsson    10 21 15   Btw it is really cool to see julia running at 400  CPU when running a list comprehension.  I did some more benchmarks with larger N to reduce the noise a bit and the difference is actually not that great between Matlab and Julia. However, tying with Matlabs parallellized vectorized maps is great imho.  julia   time h, f   Jakes_Flat  926, 1e-6, 5000000, 0, 1, 0     0.585940 seconds  153 allocations: 495.918 MB, 12.47  gc time         tic; Jakes_Flat  926, 1E-6, 5000000, 0, 1, 0  ; toc Elapsed time is 0.609867 seconds.       Lindsey Kuper    10 21 15   It's fantastic to see some good ParallelAccelerator results  in the wild !  Thanks for sharing.  Lindsey      jcal... gmail.com    10 31 15   Since people seem to recommend de-vectorisation a lot -- I should comment on my observation that de-vectorisation in Julia often makes things slower... will get some code out and start a new topic on this...