It could be because of memory usage. I have 1 TB RAM on the machine I was
doing. If you were running into swap, it would certainly take much longer.
I will try the other version as soon as the machine is available for me to use
(some admin issues), and also look into speeding things up if possible. If the
generation of the data is in your control, you can just generate it pre-sorted
or in CSC format. I just need to check if we can shortcut pre-sorted data and
generate the sparse matrix quickly.
-viral
On 01-May-2014, at 12:59 am, Ryan Gardner <rwgard...@gmail.com> wrote:
> Hmmm. That is much better than I was getting. Thanks Viral.
>
> Was it much faster for you to create the column-index, row-index, and value
> arrays? I would still expect them to be roughly on par in terms of speed.
>
>
> On Wed, Apr 30, 2014 at 2:36 PM, Viral Shah <vi...@mayin.org> wrote:
> I ran the sprand example, and it took 290 seconds on a machine with enough
> RAM. Given that it is creating a matrix with half a billion nonzeros, this
> doesn’t sound too bad.
>
> -viral
>
>
>
> On 30-Apr-2014, at 8:48 pm, Ryan Gardner <rwgard...@gmail.com> wrote:
>
> > I've got 16GB of RAM on this machine. Largely, my question, with
> > admittedly little knowledge of the internal structure of the sparse arrays,
> > is why generating the actual SparseMatrixCSC is so much slower than
> > generating what is essentially another sparse matrix representation
> > consisting of the indices and values. (I realize that once we start
> > swapping, which will happen in my example, things slow down a ton, but even
> > the sprand I mention was slow.) Do you observe the same results? Is the
> > reason for the difference clear to someone else?
> >
> > Thanks for all the comments. These are helpful. It had not crossed my
> > mind that I could control the data type of the indices.
> >
> > Using the SparseMatrixCSC constructor directly would probably be very
> > helpful. Do you learn about that constructor from looking at source code
> > or do you see it somewhere else?
> >
> > I'm also curious about where @inbounds was used.
> >
> >
> >
> >
> >
> >
> > On Wed, Apr 30, 2014 at 8:59 AM, Tony Kelman <t...@kelman.net> wrote:
> > If you're assembling the matrix in row-sorted column-major order and
> > there's no duplication, then you can also skip the conversion work by using
> > the SparseMatrixCSC constructor directly.
> >
> >
> > On Wednesday, April 30, 2014 1:10:31 AM UTC-7, Viral Shah wrote:
> > Could you post your code? Will avoid me writing the same. :-)
> >
> > Was building the vectors taking all the time, or was it in building the
> > sparse matrix from the triples? Triples to CSC conversion is an expensive
> > operation, and we have spent a fair amount of time making it fast. Of
> > course, there could be more opportunities at speeding the code.
> >
> > Where did you use @inbounds and @simd?
> >
> > -viral
> >
> >
> >
> > On 30-Apr-2014, at 1:11 pm, Dominique Orban <dominiq...@gmail.com> wrote:
> >
> > > Downgrading the 700,000 to 70,000 for the sake of not waiting all night,
> > > the original implementation takes about 4.3 seconds on my laptop.
> > > Preallocating arrays and using @inbounds brings it down to about 0.6
> > > seconds. @simd doesn't seem to provide any further speedup. Building the
> > > sparse matrix takes about 3.8 seconds. This may be due to conversion from
> > > triple to csc format?!
> > >
> > > ps: using the original size of 700,000, Julia reports a memory usage of
> > > 11.8GB.
> > >
> > >
> > > On Wednesday, April 30, 2014 12:26:02 AM UTC-7, Viral Shah wrote:
> > > I believe the memory requirement should be 700000*700*16 (64-bit nonzeros
> > > and row indices) + 700001*8 (64-bit column pointers) = 7.8 GB.
> > >
> > > This can be brought down a bit by using 32-bit index values and 64-bit
> > > floats, but then you need 5.8 GB. Finally, if you use 32-bit index values
> > > with 32-bit floats, you can come down to 4GB. The Julia sparse matrix
> > > implementation is quite flexible and allows you to easily do such things.
> > >
> > >
> > > julia> s = sparse(int32(1:10), int32(1:10), 1.0);
> > >
> > > julia> typeof(s)
> > > SparseMatrixCSC{Float64,Int32} (constructor with 1 method)
> > >
> > > julia> s = sparse(int32(1:10), int32(1:10), float32(1.0));
> > >
> > > julia> typeof(s)
> > > SparseMatrixCSC{Float32,Int32} (constructor with 1 method)
> > >
> > >
> > > -viral
> > >
> > > On Wednesday, April 30, 2014 12:36:17 PM UTC+5:30, Ivar Nesje wrote:
> > > Sorry for pointing out a probably obvious problem, but as there are
> > > others that might try debug this issue on their laptop, I ask how much
> > > memory do you have? 700000*700 floats + indexes, will spend a minimum of
> > > 11 GB (if my math is correct) and possibly more if the asymptotic storage
> > > requirement is more than 2 Int64 + 1 Float64 per stored value.
> > >
> > > Ivar
> > >
> > > kl. 01:46:22 UTC+2 onsdag 30. april 2014 skrev Ryan Gardner følgende:
> > > Creating sparse arrays seems exceptionally slow.
> > >
> > > I can set up the non-zero data of the array relatively quickly. For
> > > example, the following code takes about 80 seconds on one machine.
> > >
> > >
> > > vec_len = 700000
> > >
> > >
> > > row_ind = Uint64[]
> > > col_ind = Uint64[]
> > > value = Float64[]
> > >
> > >
> > > for j = 1:700000
> > > for k = 1:700
> > > ind = k*50
> > > push!(row_ind, ind)
> > > push!(col_ind, j)
> > > push!(value, 5.0)
> > > end
> > > end
> > >
> > >
> > > but then
> > >
> > > a = sparse(row_ind, col_ind, value, 700000, 700000)
> > >
> > >
> > > takes more than at least about 30 minutes. (I never let it finish.)
> > >
> > > It doesn't seem like the numbers I'm using should be that far off the
> > > scale. Is there a more efficient way I should be doing what I'm doing?
> > > Am I missing something and asking for something that really is
> > > impractical?
> > >
> > > If not, I may be able to look into the sparse matrix code a little this
> > > weekend.
> > >
> > >
> > > The never-finishing result is the same if I try
> > >
> > > sprand(700000, 700000, .001)
> > >
> > > or if I try to set 700000*700 values in a sparse matrix of zeros
> > > directly. Thanks.
> > >
> > >
> >
> >
>
>
