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 <[email protected]> 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. > >
