The SparseMatrixCSC constructor currently has the signature SparseMatrixCSC(m, n, colptr, rowval, nzval) It looks as though this isn't formally documented, but it's a pretty clear implementation if you understand the basics of the CSC format (and remember that all the indexing is 1-based if you've worked with sparse matrices a lot in C or Python).
In this particular case, you are constructing things in the proper order so you can easily assemble colptr and don't need to rearrange rowval or nzval at all. Note that there was a recently opened pull request https://github.com/JuliaLang/julia/pull/6696 that could change some of this if it gets merged, so maybe don't get too attached to any use of this constructor. On Wednesday, April 30, 2014 9:31:45 AM UTC-7, Dominique Orban wrote: > > Sorry, here's my code: https://gist.github.com/11431891 > > I don't see how to use SparseMatrixCSC directly. Doesn't it require that > the arrays already represent the CSC structure? > > On Wednesday, April 30, 2014 8:40:20 AM UTC-7, Viral Shah wrote: >> >> Octave 3.6 just gave up: >> >> octave:1> tic; sprand(700000, 700000, .001); toc; >> error: memory exhausted or requested size too large for range of Octave's >> index type -- trying to return to prompt >> >> >> -viral >> >> >> >> On 30-Apr-2014, at 9:08 pm, Viral Shah <[email protected]> wrote: >> >> > You can call SparseMatrixCSC directly, but then you have to do all the >> arrangement and sorting yourself. Depending on your application and how the >> nonzeros are generated, this may or may not help. >> > >> > I will investigate this further. I now have all the information I need. >> > >> > Thanks, >> > >> > -viral >> > >> > >> > >> > On 30-Apr-2014, at 8:48 pm, Ryan Gardner <[email protected]> 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 <[email protected]> >> 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 <[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. >> >>> >> >>> >> >> >> >> >> > >> >>
