615 research outputs found
ELSI: A Unified Software Interface for Kohn-Sham Electronic Structure Solvers
Solving the electronic structure from a generalized or standard eigenproblem
is often the bottleneck in large scale calculations based on Kohn-Sham
density-functional theory. This problem must be addressed by essentially all
current electronic structure codes, based on similar matrix expressions, and by
high-performance computation. We here present a unified software interface,
ELSI, to access different strategies that address the Kohn-Sham eigenvalue
problem. Currently supported algorithms include the dense generalized
eigensolver library ELPA, the orbital minimization method implemented in
libOMM, and the pole expansion and selected inversion (PEXSI) approach with
lower computational complexity for semilocal density functionals. The ELSI
interface aims to simplify the implementation and optimal use of the different
strategies, by offering (a) a unified software framework designed for the
electronic structure solvers in Kohn-Sham density-functional theory; (b)
reasonable default parameters for a chosen solver; (c) automatic conversion
between input and internal working matrix formats, and in the future (d)
recommendation of the optimal solver depending on the specific problem.
Comparative benchmarks are shown for system sizes up to 11,520 atoms (172,800
basis functions) on distributed memory supercomputing architectures.Comment: 55 pages, 14 figures, 2 table
An Optimized and Scalable Eigensolver for Sequences of Eigenvalue Problems
In many scientific applications the solution of non-linear differential
equations are obtained through the set-up and solution of a number of
successive eigenproblems. These eigenproblems can be regarded as a sequence
whenever the solution of one problem fosters the initialization of the next. In
addition, in some eigenproblem sequences there is a connection between the
solutions of adjacent eigenproblems. Whenever it is possible to unravel the
existence of such a connection, the eigenproblem sequence is said to be
correlated. When facing with a sequence of correlated eigenproblems the current
strategy amounts to solving each eigenproblem in isolation. We propose a
alternative approach which exploits such correlation through the use of an
eigensolver based on subspace iteration and accelerated with Chebyshev
polynomials (ChFSI). The resulting eigensolver is optimized by minimizing the
number of matrix-vector multiplications and parallelized using the Elemental
library framework. Numerical results show that ChFSI achieves excellent
scalability and is competitive with current dense linear algebra parallel
eigensolvers.Comment: 23 Pages, 6 figures. First revision of an invited submission to
special issue of Concurrency and Computation: Practice and Experienc
Parallel eigensolvers in plane-wave Density Functional Theory
We consider the problem of parallelizing electronic structure computations in
plane-wave Density Functional Theory. Because of the limited scalability of
Fourier transforms, parallelism has to be found at the eigensolver level. We
show how a recently proposed algorithm based on Chebyshev polynomials can scale
into the tens of thousands of processors, outperforming block conjugate
gradient algorithms for large computations
Large scale ab initio calculations based on three levels of parallelization
We suggest and implement a parallelization scheme based on an efficient
multiband eigenvalue solver, called the locally optimal block preconditioned
conjugate gradient LOBPCG method, and using an optimized three-dimensional (3D)
fast Fourier transform (FFT) in the ab initio}plane-wave code ABINIT. In
addition to the standard data partitioning over processors corresponding to
different k-points, we introduce data partitioning with respect to blocks of
bands as well as spatial partitioning in the Fourier space of coefficients over
the plane waves basis set used in ABINIT. This k-points-multiband-FFT
parallelization avoids any collective communications on the whole set of
processors relying instead on one-dimensional communications only. For a single
k-point, super-linear scaling is achieved for up to 100 processors due to an
extensive use of hardware optimized BLAS, LAPACK, and SCALAPACK routines,
mainly in the LOBPCG routine. We observe good performance up to 200 processors.
With 10 k-points our three-way data partitioning results in linear scaling up
to 1000 processors for a practical system used for testing.Comment: 8 pages, 5 figures. Accepted to Computational Material Scienc
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
- …