Hello,
I am presently working with FEAST to find eigenvalues and eigenvectors for a symmetric matrix. I need to solve N X N matrix with N ~ 10^6- 10^8.
Now I have few queries :
1. SInce the size is large it is not possible to allocate this storage in a desktop (it has 8GB ram). Is there any way to handle large matrix of this size ?
2. The matrix is also expected to be sparse for which I expect to store it in a compressed format and that can save some memory space. But the eigenvector matrix is also of the dimension N X N which I have to pre-allocate before calling FEAST. Hence the compressed storage will not be of much help. Is there any way to solve this problem ?
3. Since FEAST fpm uses 64 iparm of MKL_pardiso, I have checked that iparm(60) helps to work using disk space storage. Can I use that in feast to solve this large problem ? However, in this case also I guess I have to pass eigenvectors (N X N) to FEAST which I have to pre-allocate. Can I use disk space somehow for this?
My program is working for moderate size matrices (10000 X 10000).
I would appreciate any help in this regard.
Thanks,
Dhiraj