High-Performance Solvers for Dense Hermitian Eigenproblems

We introduce a new collection of solvers - subsequently called EleMRRR - for large-scale dense Hermitian eigenproblems. EleMRRR solves various types of problems: generalized, standard, and tridiagonal eigenproblems. Among these, the last is of particular importance as it is a solver on its own right, as well as the computational kernel for the first two; we present a fast and scalable tridiagonal solver based on the Algorithm of Multiple Relatively Robust Representations - referred to as PMRRR. Like the other EleMRRR solvers, PMRRR is part of the freely available Elemental library, and is designed to fully support both message-passing (MPI) and multithreading parallelism (SMP). As a result, the solvers can equally be used in pure MPI or in hybrid MPI-SMP fashion. We conducted a thorough performance study of EleMRRR and ScaLAPACK's solvers on two supercomputers. Such a study, performed with up to 8,192 cores, provides precise guidelines to assemble the fastest solver within the ScaLAPACK framework; it also indicates that EleMRRR outperforms even the fastest solvers built from ScaLAPACK's components

Out-of-core solution of eigenproblems for macromolecular simulations

We consider the solution of large-scale eigenvalue problems that appear in the motion simulation of complex macromolecules on desktop platforms. To tackle the dimension of the matrices that are involved in these problems, we formulate out-of-core (OOC) variants of the two selected eigensolvers, that basically decouple the performance of the solver from the storage capacity. Furthermore, we contend with the high computational complexity of the solvers by off-loading the arithmetically-intensive parts of the algorithms to a hardware graphics accelerator

Solving Dense Generalized Eigenproblems on Multi-threaded Architectures

We compare two approaches to compute a fraction of the spectrum of dense symmetric definite generalized eigenproblems: one is based on the reduction to tridiagonal form, and the other on the Krylov-subspace iteration. Two large-scale applications, arising in molecular dynamics and material science, are employed to investigate the contributions of the application, architecture, and parallelism of the method to the performance of the solvers. The experimental results on a state-of-the-art 8-core platform, equipped with a graphics processing unit (GPU), reveal that in realistic applications, iterative Krylov-subspace methods can be a competitive approach also for the solution of dense problems

Performance analysis and comparison of parallel eigensolvers on Blue Gene architectures

The solution of eigenproblems with dense, symmetric system matrices is a core task in many felds of computational science and engineering. As the problem complexity and thus the size of the matrices involved increases, the application of distributed memory supercomputer architectures and parallel algorithms becomes inevitable. Nearly all modern algorithms for eigensolving implement a tridiagonal reduction of the eigenproblem system matrix and a subsequentsolution of the tridigonalized eigenproblem. Additionally, back transformation of the eigenvectors is required if these are of interest. In the context of this thesis, implementations of two basically different approaches to the parallelsolution of eigenproblems were benchmarked, reviewed and compared with particular regard to their performance on the Blue Gene/P and Blue Gene/Q supercomputers JUGENE and JUQUEEN at the Forschungszentrum Jülich: ELPA, which implements an optimized version of the divide and conquer algorithm and Elemental which utilizes the PMRRR implementation of the MR3 algorithm. ELPA features two fundamentally different kinds of tridiagonalization, the standard one-stage and a two-stage approach. The comparision of thetwo-stage to the direct reduction was a primary concern in the performance analysis