Hello, I am a junior PhD student in theoretical physics. I am working on a DMRG (https://en.wikipedia.org/wiki/Density_matrix_renormalization_group) algorithm in Julia.

A key part of the algorithm is singular value decomposition (SVD) and diagonalization (Lanczos or Arnoldi) on large sparse matrices (MxM where M = d^2*chi^2, chi=10^2-10^4 and d = 2-10 so that M is of the order 10^6-10^10 in worst case).

It seems to me like I need to make sure that the code can work with arrays which don’t fit in memory, and Blocks.jl or ScaLAPACK bindings looks promising. Ideally, I think it would benefit from parallelization and GPU since at least some operations are embarrasingly parallel.

But I am very new to Julia and have only had a brief introduction to MPI: does anyone have recommendations for which tools and design could be useful for this problem?

Regards,

Axel Gagge