In advance I am sorry for my noobish question. I am a physics PHD student and basically I use python for my math/physics problems. But now I have a problem which requires more computing capacity and I intend to use Fortran or C and supercomputer. Those things are very new to me :).
So my problem is:
- I have a system of 17576 atoms and real symmetric weighted hessian m atrix which describes interatomic interactions and has 52728x52728=2780241984 elements.
- This matrix is very sparse: 1.56% of elements are nonzero
- The minimum and maximum of eigenvalues and density of states are known
- I need to diagonalise this matrix and find all eigenvalues and eigenvectors with high precision
I heard that there is FEAST and SLEPc. However SLEPc is not suitable for whole eigenspectrum calculations. Is it ok to use FEAST for this kind of problem or I should use something different? In FEAST you can define search interval and in documentation it is written that high accuracy can be obtained only for up to 1000 eigenpairs. So my idea is to calculate whole eigenspectrum by splitting this problem into multiple intervals with less than 1000 eigenvalues. Or my intuition is wrong about how should I approach this problem?
So in summary my questions are:
- Is it OK to use FEAST for this kind of problem or I should use something different? Any other recommendations are very acceptable
- If FEAST is suitable for this problem, how it scales and how should I distribute resources?
- Also suggestions about what should I read or learn is very appreciated, because I don't know from where to start and I don't have anyone to consult
Thanks for Your attention