Realy ? Is imposibly smoething like :
take 1. column and copmute on 1. core, wihout waiting for end of 1.
oparation take 2. column and copmpute on 2. cores .etc.... ?
Paul
W dniu 2015-01-31 o 16:32, Tim Holy pisze:
Paul, until the "threads" branch gets merged, I recommend that you just accept
the fact that you'll only have 1 core active for most operations.
--Tim
On Saturday, January 31, 2015 07:15:25 AM paul analyst wrote:
Thx, but, no.
For sparse matrix 10^5,10^4,0.002 is the same . Time for both whiles is
about 48 sek, only 11% o cores is used. I vave 8 cores, 7 sleeps:/
Paul
W dniu sobota, 31 stycznia 2015 15:50:02 UTC+1 użytkownik Sam Kaplan
napisał:
Hi Paul,
If D is allocated on the master, then Julia will need to pass D from the
master to the workers. I'm guessing that this communication might be more
expensive than the compute in your loops. It may be useful to take a look
at distributed arrays in the parallel section of the Julia docs.
Hope it helps.
Sam
On Saturday, January 31, 2015 at 7:38:22 AM UTC-6, paul analyst wrote:
Parallel loop, what wroong ? Parallel is slower then normal
julia> @time for i=1:l
w[i]=var(D[:,i])
end
elapsed time: 4.443197509 seconds (14074576 bytes allocated)
julia> @time ww=@parallel (hcat) for i=1:l
var(D[:,i])
end
elapsed time: 5.287007403 seconds (435449580 bytes allocated, 5.00% gc
time)
1x10000 Array{Float64,2}:
Paul
julia> @time for i=1:l
w[i]=var(D[:,i])
end
elapsed time: 4.331569152 seconds (8637464 bytes allocated)
julia> @time ww=@parallel (hcat) for i=1:l
var(D[:,i])
end
elapsed time: 4.908234336 seconds (422121448 bytes allocated, 4.85% gc
time)
1x10000 Array{Float64,2}:
0.000703737 0.000731674 0.000582672 0.00080388 0.000759479
0.000402509 0.0007118 0.000989408
julia> size(D)
(10000,10000)
