SIESTA‐SIPs: Massively parallel spectrum‐slicing eigensolver for an ab initio molecular dynamics package

Integration of Shift‐and‐Invert Parallel Spectral Transformation (SIPs) eigensolver (as implemented in the SLEPc library) into an ab initio molecular dynamics package, SIESTA, is described. The effectiveness of the code is demonstrated on applications to polyethylene chains, boron nitride sheets, and bulk water clusters. For problems with the same number of orbitals, the performance of the SLEPc eigensolver depends on the sparsity of the matrices involved, favoring reduced dimensional systems such as polyethylene or boron nitride sheets in comparison to bulk systems like water clusters. For all problems investigated, performance of SIESTA‐SIPs exceeds the performance of SIESTA with default solver (ScaLAPACK) at the larger number of cores and the larger number of orbitals. A method that improves the load‐balance with each iteration in the self‐consistency cycle by exploiting the emerging knowledge of the eigenvalue spectrum is demonstrated. © 2018 Wiley Periodicals, Inc. Matrix diagonalization is often the bottleneck of scalability for electronic structure codes based on density‐functional theory. Implementation details and benchmark results are presented for a scalable sparse eigensolver integrated into SIESTA ab initio molecular dynamics package.