Parallel implementation of electronic structure eigensolver using a partitioned folded spectrum method

A parallel implementation of an eigensolver designed for electronic structure calculations is presented. The method is applicable to computational tasks that solve a sequence of eigenvalue problems where the solution for a particular iteration is similar but not identical to the solution from the previous iteration. Such problems occur frequently when performing electronic structure calculations in which the eigenvectors are solutions to the Kohn-Sham equations. The eigenvectors are represented in some type of basis but the problem sizes are normally too large for direct diagonalization in that basis. Instead a subspace diagonalization procedure is employed in which matrix elements of the Hamiltonian operator are generated and the eigenvalues and eigenvectors of the resulting reduced matrix are obtained using a standard eigensolver from a package such as LAPACK or SCALAPACK. While this method works well and is widely used, the standard eigensolvers scale poorly on massively parallel computer systems for the matrix sizes typical of electronic structure calculations. We present a new method that utilizes a partitioned folded spectrum scheme (PFSM) that takes into account the iterative nature of the problem and performs well on massively parallel systems. Test results for a range of problems are presented that demonstrate an equivalent level of accuracy when compared to the standard eigensolvers, while also executing up to an order of magnitude faster. Unlike O(N) methods, the technique works equally well for metals and systems with unoccupied orbitals as for insulators and semiconductors. Timing and accuracy results are presented for a range of systems, including a 512 atom diamond cell, a cluster of 13 C60 molecules, bulk copper, a 216 atom silicon cell with a vacancy, using 40 unoccupied states/atom, and a 4000 atom aluminum supercell.