If you're careful and know how the compressed sparse column format works,
you can try generating the matrix column by column, doing in-place sorting
of the row indices within each column. That would make it straightforward
to construct the colptr and rowval vectors, and use the SparseMatrixCSC
With 24GB RAM, 12G for the input, and almost the same again for the sparse
matrix generated will certainly run it dangerously close to the total
memory.
-viral
On Tuesday, October 7, 2014 9:30:27 PM UTC+5:30, Iain Dunning wrote:
>
> Yes I think your estimate is correct:
>
> N => 10001
> k => 50
Yes I think your estimate is correct:
N => 10001
k => 500080001
num_bytes => 12001920024
num_bytes / 1024^3 => 11.177659057080746
When I run the code, it doesn't have any trouble allocating the 3 arrays (I
have 16GB ram) and populating them (its pretty quick too), but it "freezes"
when it gets
Hi Iain,
First of all thanks for your effort!
I am using Julia 0.3.0 on Linux. The reason i wasn't preallocating was
because I did not know the array sizes beforehand, but your suggestion of
computing it works very well :)
I just noticed that in my original post I put 1000 instead of 1. So,
OK, on Julia 0.3.0 on OSX, for N=1001
elapsed time: 0.722537543 seconds (960169036 bytes allocated, 11.71% gc
time)
elapsed time: 0.703546159 seconds (955424928 bytes allocated, 26.01% gc
time)
elapsed time: 0.692751989 seconds (955424928 bytes allocated, 23.39% gc
time)
(first run includes JIT)
At a glance, why build list only to just add it to J? Why not add it
directly to J, and add i to I.
If I have a chance I'll look further.
Which Julia?
On Monday, October 6, 2014 1:51:36 PM UTC-4, Andrei Berceanu wrote:
>
> I have written the following Julia code to build a sparse matrix of
> d
