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?
>>>
>>
