Ah, I see that this particular issue is only related to the example on the small matrix - where the memory utilization is certainly due to compilation. Over and above that, for larger problems, there will other memory issues that I mentioned in my earlier email.
-viral > On 07-Feb-2015, at 1:18 am, Tim Holy <[email protected]> wrote: > > Since it allocates 120MB on the first call and only 9kB on the second, all > that > memory is just due to compilation. > > The only way I know to fix that is to precompile some of the functions when > you > build julia. If this is a big deal to you, consider adding your own custom > userimg.jl (and see base/precompile.jl for a model). > > http://docs.julialang.org/en/latest/manual/modules/?highlight=userimg#module-initialization-and-precompilation > > --Tim > > On Friday, February 06, 2015 11:23:52 AM Viral Shah wrote: >> This is because the internal operations with svds are allocating new >> vectors every iteration. This PR should address it, but needs to be >> completed, >> >> https://github.com/JuliaLang/julia/pull/7907 >> >> Also, array indexing giving views by default will also help here. >> >> -viral >> >> On Friday, February 6, 2015 at 7:50:24 PM UTC+5:30, Todd Leo wrote: >>> Hi Julians, >>> >>> I am trying to apply svd on a very large sparse matrix using svds. When >>> warming up svds function, I used a very small random vector, and the >>> number >>> of singular values (nsv) is set to one. However, this simple set up >>> results >>> in a considerable high cost of time, and as much as 120MB memory is >>> occupied. To me, it doesn't make sense, and one can reproduce it by: >>> >>> julia> @time svds(sprand(3,3,0.1), nsv = 1) >>> >>>> elapsed time: 2.640233094 seconds (117 MB allocated, 1.06% gc time in 5 >>>> >>>>> pauses with 0 full sweep) >>> >>> Regards, >>> Todd >
