And I am pretty excited that it seems to scale so well at your setup. I have only 2 cores so could not see if it scales to more cores.
Am Sonntag, 18. Mai 2014 16:40:18 UTC+2 schrieb Tobias Knopp: > > Well when I started I got segfaullt all the time :-) > > Could you please send me a minimal code example that segfaults? This would > be great! This is the only way we can get this stable. > > Am Sonntag, 18. Mai 2014 16:35:47 UTC+2 schrieb Carlos Becker: >> >> Sounds great! >> I just gave it a try, and with 16 threads I get 0.07sec which is >> impressive. >> >> That is when I tried it in isolated code. When put together with other >> julia code I have, it segfaults. Have you experienced this as well? >> Le 18 mai 2014 16:05, "Tobias Knopp" <tobias...@googlemail.com> a >> écrit : >> >>> sure, the function is Base.parapply though. I had explicitly imported it. >>> >>> In the case of vectorize_1arg it would be great to automatically >>> parallelize comprehensions. If someone could tell me where the actual >>> looping happens, this would be great. I have not found that yet. Seems to >>> be somewhere in the parser. >>> >>> Am Sonntag, 18. Mai 2014 14:30:49 UTC+2 schrieb Carlos Becker: >>>> >>>> btw, the code you just sent works as is with your pull request branch? >>>> >>>> >>>> ------------------------------------------ >>>> Carlos >>>> >>>> >>>> On Sun, May 18, 2014 at 1:04 PM, Carlos Becker <carlos...@gmail.com>wrote: >>>> >>>>> HI Tobias, I saw your pull request and have been following it closely, >>>>> nice work ;) >>>>> >>>>> Though, in the case of element-wise matrix operations, like tanh, >>>>> there is no need for extra allocations, since the buffer should be >>>>> allocated only once. >>>>> >>>>> From your first code snippet, is julia smart enough to pre-compute >>>>> i*N/2 ? >>>>> In such cases, creating a kind of array view on the original data >>>>> would probably be faster, right? (though I don't know how allocations >>>>> work >>>>> here). >>>>> >>>>> For vectorize_1arg_openmp, I was thinking of "hard-coding" it for >>>>> known operations such as trigonometric ones, that benefit a lot from >>>>> multi-threading. >>>>> I know this is a hack, but it is quick to implement and brings an >>>>> amazing speed up (8x in the case of the code I posted above). >>>>> >>>>> >>>>> >>>>> >>>>> ------------------------------------------ >>>>> Carlos >>>>> >>>>> >>>>> On Sun, May 18, 2014 at 12:30 PM, Tobias Knopp < >>>>> tobias...@googlemail.com> wrote: >>>>> >>>>>> Hi Carlos, >>>>>> >>>>>> I am working on something that will allow to do multithreading on >>>>>> Julia functions (https://github.com/JuliaLang/julia/pull/6741). >>>>>> Implementing vectorize_1arg_openmp is actually a lot less trivial as the >>>>>> Julia runtime is not thread safe (yet) >>>>>> >>>>>> Your example is great. I first got a slowdown of 10 because the >>>>>> example revealed a locking issue. With a little trick I now get a >>>>>> speedup >>>>>> of 1.75 on a 2 core machine. Not to bad taking into account that memory >>>>>> allocation cannot be parallelized. >>>>>> >>>>>> The tweaked code looks like >>>>>> >>>>>> function tanh_core(x,y,i) >>>>>> >>>>>> N=length(x) >>>>>> >>>>>> for l=1:N/2 >>>>>> >>>>>> y[l+i*N/2] = tanh(x[l+i*N/2]) >>>>>> >>>>>> end >>>>>> >>>>>> end >>>>>> >>>>>> >>>>>> function ptanh(x;numthreads=2) >>>>>> >>>>>> y = similar(x) >>>>>> >>>>>> N = length(x) >>>>>> >>>>>> parapply(tanh_core,(x,y), 0:1, numthreads=numthreads) >>>>>> >>>>>> y >>>>>> >>>>>> end >>>>>> >>>>>> >>>>>> I actually want this to be also fast for >>>>>> >>>>>> >>>>>> function tanh_core(x,y,i) >>>>>> >>>>>> y[i] = tanh(x[i]) >>>>>> >>>>>> end >>>>>> >>>>>> >>>>>> function ptanh(x;numthreads=2) >>>>>> >>>>>> y = similar(x) >>>>>> >>>>>> N = length(x) >>>>>> >>>>>> parapply(tanh_core,(x,y), 1:N, numthreads=numthreads) >>>>>> >>>>>> y >>>>>> >>>>>> end >>>>>> >>>>>> Am Sonntag, 18. Mai 2014 11:40:13 UTC+2 schrieb Carlos Becker: >>>>>> >>>>>>> now that I think about it, maybe openblas has nothing to do here, >>>>>>> since @which tanh(y) leads to a call to vectorize_1arg(). >>>>>>> >>>>>>> If that's the case, wouldn't it be advantageous to have a >>>>>>> vectorize_1arg_openmp() function (defined in C/C++) that works for >>>>>>> element-wise operations on scalar arrays, >>>>>>> multi-threading with OpenMP? >>>>>>> >>>>>>> >>>>>>> El domingo, 18 de mayo de 2014 11:34:11 UTC+2, Carlos Becker >>>>>>> escribió: >>>>>>>> >>>>>>>> forgot to add versioninfo(): >>>>>>>> >>>>>>>> julia> versioninfo() >>>>>>>> Julia Version 0.3.0-prerelease+2921 >>>>>>>> Commit ea70e4d* (2014-05-07 17:56 UTC) >>>>>>>> Platform Info: >>>>>>>> System: Linux (x86_64-linux-gnu) >>>>>>>> CPU: Intel(R) Xeon(R) CPU X5690 @ 3.47GHz >>>>>>>> WORD_SIZE: 64 >>>>>>>> BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY) >>>>>>>> LAPACK: libopenblas >>>>>>>> LIBM: libopenlibm >>>>>>>> >>>>>>>> >>>>>>>> El domingo, 18 de mayo de 2014 11:33:45 UTC+2, Carlos Becker >>>>>>>> escribió: >>>>>>>>> >>>>>>>>> This is probably related to openblas, but it seems to be that >>>>>>>>> tanh() is not multi-threaded, which hinders a considerable speed >>>>>>>>> improvement. >>>>>>>>> For example, MATLAB does multi-thread it and gets something around >>>>>>>>> 3x speed-up over the single-threaded version. >>>>>>>>> >>>>>>>>> For example, >>>>>>>>> >>>>>>>>> x = rand(100000,200); >>>>>>>>> @time y = tanh(x); >>>>>>>>> >>>>>>>>> yields: >>>>>>>>> - 0.71 sec in Julia >>>>>>>>> - 0.76 sec in matlab with -singleCompThread >>>>>>>>> - and 0.09 sec in Matlab (this one uses multi-threading by >>>>>>>>> default) >>>>>>>>> >>>>>>>>> Good news is that julia (w/openblas) is competitive with matlab >>>>>>>>> single-threaded version, >>>>>>>>> though setting the env variable OPENBLAS_NUM_THREADS doesn't have >>>>>>>>> any effect on the timings, nor I see higher CPU usage with 'top'. >>>>>>>>> >>>>>>>>> Is there an override for OPENBLAS_NUM_THREADS in julia? what am I >>>>>>>>> missing? >>>>>>>>> >>>>>>>> >>>>> >>>>
