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poulson on master
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Sorry; I have not been active on this project in a long time but will try to respond to the extreme backlog.
@wlandry Yes, DiagonalScale (from the left) can be used for
A(i, j) := d(i) * A(i, j) or (from the right)
A(i, j) := A(i, j) * d(j). There are equivalent versions for triangular and Hessenberg matrices. And Hadamard performs an elementwise multiply (i.e.,
C(i, j) = A(i, j) * B(i, j)).
El::IndexDependentFill should be automatically parallelized over each process's local matrix if you are using a distributed matrix. But I am guessing that you are thinking of a local matrix and hoping for threaded parallelism. Threading was never a part of Elemental's focus.
@avaziria @jeffhammond worked on something called BigMPI to help with this but I do not recall the status. Maybe (probably) he could say more.
@Belliger Sorry to see all of the trouble: it is strongly preferred to use the auto-generated CMake (or, as a fallback, Make) files generated during Elemental's configuration to build your project. Many toolchains are unfortunately entirely incompatible.
This is most likely a very simple question for the experts here, but I am new to using non-standard data types in C++. Part of my code produces numbers which are defined using the mpreal data type from MPFR (this cannot be changed unfortunately). I would like to redefine these as DoubleDouble from the QD library in order to build a matrix for use in the Elemental library. I cannot seem to figure out how to do this.
Another question I have is about the performance of Elemental at higher than double precision? Obviously the performance will take a big hit but I moved to Elemental from the Eigen library as Eigen does not play well with custom types (mpreal) as re-allocations for these types reduce the speed by a factor of 100+. Has anyone had experience using Elemental (specifically generalized eigenvalue calculation) at higher precision (DoubleDouble, QuadDouble etc...)