278 research outputs found
On acceleration of Krylov-subspace-based Newton and Arnoldi iterations for incompressible CFD: replacing time steppers and generation of initial guess
We propose two techniques aimed at improving the convergence rate of steady
state and eigenvalue solvers preconditioned by the inverse Stokes operator and
realized via time-stepping. First, we suggest a generalization of the Stokes
operator so that the resulting preconditioner operator depends on several
parameters and whose action preserves zero divergence and boundary conditions.
The parameters can be tuned for each problem to speed up the convergence of a
Krylov-subspace-based linear algebra solver. This operator can be inverted by
the Uzawa-like algorithm, and does not need a time-stepping. Second, we propose
to generate an initial guess of steady flow, leading eigenvalue and eigenvector
using orthogonal projection on a divergence-free basis satisfying all boundary
conditions. The approach, including the two proposed techniques, is illustrated
on the solution of the linear stability problem for laterally heated square and
cubic cavities
Block Locally Optimal Preconditioned Eigenvalue Xolvers (BLOPEX) in hypre and PETSc
We describe our software package Block Locally Optimal Preconditioned
Eigenvalue Xolvers (BLOPEX) publicly released recently. BLOPEX is available as
a stand-alone serial library, as an external package to PETSc (``Portable,
Extensible Toolkit for Scientific Computation'', a general purpose suite of
tools for the scalable solution of partial differential equations and related
problems developed by Argonne National Laboratory), and is also built into {\it
hypre} (``High Performance Preconditioners'', scalable linear solvers package
developed by Lawrence Livermore National Laboratory). The present BLOPEX
release includes only one solver--the Locally Optimal Block Preconditioned
Conjugate Gradient (LOBPCG) method for symmetric eigenvalue problems. {\it
hypre} provides users with advanced high-quality parallel preconditioners for
linear systems, in particular, with domain decomposition and multigrid
preconditioners. With BLOPEX, the same preconditioners can now be efficiently
used for symmetric eigenvalue problems. PETSc facilitates the integration of
independently developed application modules with strict attention to component
interoperability, and makes BLOPEX extremely easy to compile and use with
preconditioners that are available via PETSc. We present the LOBPCG algorithm
in BLOPEX for {\it hypre} and PETSc. We demonstrate numerically the scalability
of BLOPEX by testing it on a number of distributed and shared memory parallel
systems, including a Beowulf system, SUN Fire 880, an AMD dual-core Opteron
workstation, and IBM BlueGene/L supercomputer, using PETSc domain decomposition
and {\it hypre} multigrid preconditioning. We test BLOPEX on a model problem,
the standard 7-point finite-difference approximation of the 3-D Laplacian, with
the problem size in the range .Comment: Submitted to SIAM Journal on Scientific Computin
Block Iterative Eigensolvers for Sequences of Correlated Eigenvalue Problems
In Density Functional Theory simulations based on the LAPW method, each
self-consistent field cycle comprises dozens of large dense generalized
eigenproblems. In contrast to real-space methods, eigenpairs solving for
problems at distinct cycles have either been believed to be independent or at
most very loosely connected. In a recent study [7], it was demonstrated that,
contrary to belief, successive eigenproblems in a sequence are strongly
correlated with one another. In particular, by monitoring the subspace angles
between eigenvectors of successive eigenproblems, it was shown that these
angles decrease noticeably after the first few iterations and become close to
collinear. This last result suggests that we can manipulate the eigenvectors,
solving for a specific eigenproblem in a sequence, as an approximate solution
for the following eigenproblem. In this work we present results that are in
line with this intuition. We provide numerical examples where opportunely
selected block iterative eigensolvers benefit from the reuse of eigenvectors by
achieving a substantial speed-up. The results presented will eventually open
the way to a widespread use of block iterative eigensolvers in ab initio
electronic structure codes based on the LAPW approach.Comment: 12 Pages, 5 figures. Accepted for publication on Computer Physics
Communication
Preconditioned Locally Harmonic Residual Method for Computing Interior Eigenpairs of Certain Classes of Hermitian Matrices
We propose a Preconditioned Locally Harmonic Residual (PLHR) method for
computing several interior eigenpairs of a generalized Hermitian eigenvalue
problem, without traditional spectral transformations, matrix factorizations,
or inversions. PLHR is based on a short-term recurrence, easily extended to a
block form, computing eigenpairs simultaneously. PLHR can take advantage of
Hermitian positive definite preconditioning, e.g., based on an approximate
inverse of an absolute value of a shifted matrix, introduced in [SISC, 35
(2013), pp. A696-A718]. Our numerical experiments demonstrate that PLHR is
efficient and robust for certain classes of large-scale interior eigenvalue
problems, involving Laplacian and Hamiltonian operators, especially if memory
requirements are tight
Hybrid preconditioning for iterative diagonalization of ill-conditioned generalized eigenvalue problems in electronic structure calculations
The iterative diagonalization of a sequence of large ill-conditioned
generalized eigenvalue problems is a computational bottleneck in quantum
mechanical methods employing a nonorthogonal basis for {\em ab initio}
electronic structure calculations. We propose a hybrid preconditioning scheme
to effectively combine global and locally accelerated preconditioners for rapid
iterative diagonalization of such eigenvalue problems. In partition-of-unity
finite-element (PUFE) pseudopotential density-functional calculations,
employing a nonorthogonal basis, we show that the hybrid preconditioned block
steepest descent method is a cost-effective eigensolver, outperforming current
state-of-the-art global preconditioning schemes, and comparably efficient for
the ill-conditioned generalized eigenvalue problems produced by PUFE as the
locally optimal block preconditioned conjugate-gradient method for the
well-conditioned standard eigenvalue problems produced by planewave methods
- …