There were several recent extensions to Elemental's SVD support, as detailed here: https://github.com/elemental/Elemental/issues/125
In particular, FULL_SVD, THIN_SVD, COMPACT_SVD, and PRODUCT_SVD are now all supported, where the latter should used if you do not need small triplets as it uses a more parallelizable reduction to tridiagonal form of A^H A rather than a reduction to bidiagonal form of A (as well as a more parallelized MRRR Hermitian tridiagonal eigensolver instead of a QR algorithm for the bidiagonal SVD). With that said, it's worth asking if your friend only needs the top k triplets for modest k, as something like Jiahao and Andreas's TSVD implementation on top of a distributed dense matrix-vector product likely be the way to go (otherwise, a wrapper to SLEPc would be warranted). Jack On Tuesday, March 8, 2016 at 12:19:07 PM UTC-8, Tony Kelman wrote: > > May also want to look into Elemental. Last I checked Elemental uses > Scalapack in a handful of places but I don't think it would be the case > here. > > > On Tuesday, March 8, 2016 at 8:19:15 AM UTC-8, Erik Schnetter wrote: >> >> A colleague mentioned to me that he needs to diagonalize (find >> eigenvalues and eigenvectors) of a symmetric dense matrix with n=10^6 >> (i.e. the matrix has 10^12 entries). ScaLapack seems to be the way to >> go for this. >> >> I'm happy to see there is >> <https://github.com/JuliaParallel/ScaLAPACK.jl>. Saying its >> documentation is "Spartan" is an understatement. There is a file >> "test/test.jl" that seems to not be included from "runtests.jl", and >> which thus might be intended as example. >> >> The package doesn't pass its tests. If you know more about whether >> this just needs a brush-up or whether major surgery is required I'd >> appreciate feedback. >> >> -erik >> >> -- >> Erik Schnetter <[email protected]> >> http://www.perimeterinstitute.ca/personal/eschnetter/ >> >
