As of now, you can use SharedArray. Eventually, once we have a good threading model, we want to multi-thread the entire array library, but that is quite some ways away.
-viral On Sunday, January 4, 2015 3:22:29 PM UTC+5:30, paul analyst wrote: > > Suppose the array is located in memory. There are a lot of columns to > count eg. Average. As a parallel process count because now 7 of 8 > processors doing nothing. > Paul > > W dniu 2015-01-03 o 23:27, [email protected] pisze: > > > > On Sunday, January 4, 2015 4:28:06 AM UTC+10, paul analyst wrote: >> >> THX >> A have not :/ but I can makes it in parts! >> > > If the arrays won't fit in memory it probably doesn't matter what Julia > does, the IO or paging time will dominate. > > Cheers > Lex > > > >> >> How simply use parallel for it? I have 8 proc, is working only 1 >> Paul >> >> >> >> >> W dniu piątek, 15 sierpnia 2014 11:53:54 UTC+2 użytkownik Billou Bielour >> napisał: >>> >>> This might be a bit faster: >>> >>> function sub!(A,B,C) >>> for j=1:size(A,2) >>> for i=1:size(A,1) >>> @inbounds C[i,j] = A[i,j] - B[i,j] >>> end >>> end >>> end >>> >>> C = zeros(size(A)); >>> sub!(A,B,C) >>> >>> Do you have enough RAM to store these matrices though ? 10^5 * 10^5 >>> Float64 seems rather large. >>> >>> >
