Efficient EigenSolver Implementation

Question

I am looking for an efficient eigensolver ( language not important, although I would be programming in C#), that utilizes the multi-core features found in modern CPU. Being able to work directly with pardiso solver is a major plus. My matrix are mostly sparse matrix, so an ideal solver should be able to take advantage of this fact and greatly enhance the memory usage and performance.

So far I have only found LAPACK and ARPACK. The LAPACK, as implemented in Intel MKL, is a good candidate, as it offers multi-core optimization. But it seems that the drivers inside the LAPACK don't work directly with pardiso solver, furthermore, it seems that they don't take advantage of sparse matrix ( but I am not sure on this point).

ARPACK, on the other hand, seems to be pretty hard to setup in Windows environment, and the parallel version, PARPACK, doesn't work so well. The bonus point is that it can work with pardiso solver.

The best would be Intel MKL + ARPACK with multi-core speedup. Not sure whether there is any existing implementations that already do what I want to do?

Answer 1

Hi, I'm working on a problem with needs very similar to the ones you state. I'm considering FEAST: http://www.ecs.umass.edu/~polizzi/feast/index.htm I'm trying to make it work right now, but it seems perfect. I'm interested in hearing what your experience with it is, if you use it. cheers Ned

Answer 2

Have a look at the Eigen2 library.

Answer 3

I've implemented it already, in C#.

The idea is that one must convert the matrix format in CSR format. Then, one can use MKL to compute linear equation solving algorithm ( using pardiso solver), the matrix-vector manipulation.