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 > >
