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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
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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.
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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...
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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(R) Core(TM) i7-4600U CPU @ 2.10GHz
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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....
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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 
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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
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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
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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
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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?
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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...
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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)
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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?
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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 : )
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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
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-- 
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 C 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):
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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 C 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 C 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 C backend). Generating C 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 C. 
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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 C 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 C 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 C backend). Generating C 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 C. 

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