Hi.
Regarding your questions, I am also having the same problems and no luck
with those {Any} and SharedArrays. Also,
I am having the same problems with those crashes, so I have to restart
Julia after finishing any parallel code.
I am looking forward to see Tim's comments about those type instabilities.
Thanks.
On Friday, July 31, 2015 at 9:47:52 AM UTC-3, thr wrote:
>
> Yes, sure.
>
> On Friday, July 31, 2015 at 2:02:25 PM UTC+2, Tim Holy wrote:
>>
>> I have a demo to answer your question, but I'd like to simply add it to
>> the
>> documentation on SharedArrays. May I use some of your code in writing up
>> the
>> demo? (MIT license, see
>>
>> https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md#improving-documentation)
>>
>>
>>
>> --Tim
>>
>> On Thursday, July 30, 2015 05:51:24 PM thr wrote:
>> > Hi all,
>> >
>> > I'm implementing a basic explicit advection algorithm of the form:
>> >
>> > for t = 1:T-1
>> > for j = 3:n-2
>> > for i = 3:m-2
>> > q[i,j,t+1]= timestep(q[i,j,t], u[i,j,t])
>> > end
>> > end
>> > end
>> >
>> >
>> > where q is a quantity and u a velocity field.
>> > I'd like to parallelize this by using sharded arrays and @parallel for,
>> I
>> > tried the following:
>> >
>> > const n = 500
>> > const m = 500
>> > const T = 500
>> >
>> > @everywhere function timestep(x,y)
>> > #return x+y
>> > return x+y +x+y +x+y +x+y +x+y +x+y +x+y
>> > end
>> >
>> > function advection_ser(q, u)
>> > println("==============serial=================$n x $m x $T")
>> > for t = 1:T-1
>> > for j = 3:n-2
>> > for i = 3:m-2
>> > q[i,j,t+1]= timestep(q[i,j,t], u[i,j,t])
>> > end
>> > end
>> > end
>> > return q
>> > end
>> >
>> > function advection_par(q,u)
>> > println("==============parallel=================$n x $m x $T")
>> > for t = 1:T-1
>> > @sync @parallel for j = 3:n-2
>> > for i = 3:m-2
>> > q[i,j,t+1]= timestep(q[i,j,t], u[i,j,t])
>> > end
>> > end
>> > end
>> > return q
>> > end
>> >
>> > q = SharedArray(Float64, (m,n,T), init=false)
>> > u = SharedArray(Float64, (m,n,T), init=false)
>> >
>> > @time qs = advection_ser(q,u)
>> > @time qp = advection_par(q,u)
>> >
>> >
>> >
>> >
>> > But this yields only a very moderate speed gain: the parallel version
>> is
>> > about 1/3 faster than the serial version for m,n,T=500,500,500 and -p
>> 4.
>> > Is there a way I can improve on this?
>> >
>> > I have also seen some weird behaviour regarding shared arrays and I'd
>> like
>> > to verify that I'm not just doing it wrong before opening issues:
>> >
>> > 1. When I construct q inside of the advection function, @code_warntype
>> > tells me that it's handled as an 'any' and the code is much slower.
>> > However, typeof(q) tells me it's of type SharedArray{Float64,3} as it
>> > should be.
>> >
>> > 2. I'm pretty sure there's a memory hole associated with SharedArrays,
>> for
>> > when I start above program over and over eventually I get a bus error
>> and
>> > julia crashes. Do I have to somehow release the shared memory from the
>> > workers?
>> >
>> > Thanks in advance, Johannes
>>
>>
