Ah! I am also a newbie, and am trying to get a sense for efficiency
improvement in julia, and how it works, so I tried a few of the combination
of suggestions here, and this doesn't make sense to me. Putting const in
front of T and RHS in the original code does not change running time much,
but it is critical for the performance of the following nested loops. With
nested loops, without const, running time is much more than the original
code. The following are the exact run times on my computer (for 100 time
steps NT = 100):
original code: 21 sec
original code with const in front if T and RHS: 20 sec
nested loops w/out const in front if T and RHS: 200 sec
nested loops with const in front if T and RHS: 3 sec
Also, @inbounds does not have much effect in any of the above cases, just a
little! Any thoughts are appreciated!
On Saturday, April 25, 2015 at 4:45:45 PM UTC-4, Kristoffer Carlsson wrote:
>
> It is definitely the slicing that is killing performance. Right now,
> slicing is expensive since you copy a whole new array for it.
>
> Putting const in front of T and RHS and using a loop like this (maybe some
> mistake but the principle is what is important) makes the code 10x faster:
>
> @inbounds for i=2:NX-1, j=2:NY-1, k=2:NZ-1
> RHS[i,j,k] = dt*A*( (T[i-1,j,k]-2*T[i,j,k]+T[i+1,j,k])/dx2 +
> (T[i,j-1,k]-2*T[i,j,k]+T[i,j+1,k])/dy2 +
> (T[i,j,k-1]-2*T[i,j,k]+T[i,j,k+1])/dz2 )
>
> T[i,j,k] = T[i,j,k] + RHS[i,j,k]
> end
>
>
>
>
> On Saturday, April 25, 2015 at 7:57:01 PM UTC+2, Johan Sigfrids wrote:
>>
>> I think it is all the slicing that is killing the performance. Maybe
>> something like arrayviews or the new sub stuff on 0.4 would help.
>> Alternatively devectorizing into a bunch of nested loops.
>>
>> On Saturday, April 25, 2015 at 8:42:09 PM UTC+3, Stefan Karpinski wrote:
>>>
>>> Stick const in front of T and RHS.
>>>
>>> On Sat, Apr 25, 2015 at 11:32 AM, Tim Holy <[email protected]> wrote:
>>>
>>>> Did you read through
>>>> http://docs.julialang.org/en/release-0.3/manual/performance-tips/? You
>>>> should
>>>> memorize :-) the sections up through the Tools section; the rest you can
>>>> consult as you discover you need them.
>>>>
>>>> --Tim
>>>>
>>>> On Saturday, April 25, 2015 01:03:38 AM Ángel de Vicente wrote:
>>>> > Hi,
>>>> >
>>>> > a complete Julia newbie here... I spent a couple of days learning the
>>>> > syntax and main aspects of Julia, and since I heard many good things
>>>> about
>>>> > it, I decided to try a little program to see how it compares against
>>>> the
>>>> > other ones I regularly use: Fortran and Python.
>>>> >
>>>> > I wrote a minimal program to solve the 3D heat equation in a cube of
>>>> > 100x100x100 points in the three languages and the time it takes to
>>>> run in
>>>> > each one is:
>>>> >
>>>> > Fortran: ~7s
>>>> > Python: ~33s
>>>> > Julia: ~80s
>>>> >
>>>> > The code runs for 1000 iterations, and I'm being nice to Julia, since
>>>> the
>>>> > programs in Fortran and Python write 100 HDF5 files with the complete
>>>> 100^3
>>>> > data (every 10 iterations).
>>>> >
>>>> > I attach the code (and you can also get it at:
>>>> http://pastebin.com/y5HnbWQ1)
>>>> >
>>>> > Am I doing something obviously wrong? Any suggestions on how to
>>>> improve its
>>>> > speed?
>>>> >
>>>> > Thanks a lot,
>>>> > Ángel de Vicente
>>>>
>>>>
>>>
