activity
20162022
most citedScheduling massively parallel multigrid for multilevel Monte Carlo methods

1 citations · 1 across the 4 of their papers we have counts for

collaborators

9 papers

math.NA2022

A matrix-free ILU realization based on surrogates

Daniel Drzisga, Andreas Wagner, Barbara Wohlmuth

Matrix-free techniques play an increasingly important role in large-scale simulations. Schur complement techniques and massively parallel multigrid solvers for second-order ellipti…

cs.CE2021

Semi matrix-free twogrid shifted Laplacian preconditioner for the Helmholtz equation with near optimal shifts

Daniel Drzisga, Tobias Köppl, Barbara Wohlmuth

Due to its significance in terms of wave phenomena a considerable effort has been put into the design of preconditioners for the Helmholtz equation. One option to derive a precondi…

cs.DC2020

Resiliency in Numerical Algorithm Design for Extreme Scale Simulations

Emmanuel Agullo, Mirco Altenbernd, Hartwig Anzt +33

This work is based on the seminar titled ``Resiliency in Numerical Algorithm Design for Extreme Scale Simulations'' held March 1-6, 2020 at Schloss Dagstuhl, that was attended by a…

math.NA2020

The surrogate matrix methodology: Accelerating isogeometric analysis of waves

Daniel Drzisga, Brendan Keith, Barbara Wohlmuth

The surrogate matrix methodology delivers low-cost approximations of matrices (i.e., surrogate matrices) which are normally computed in Galerkin methods via element-scale quadratur…

cs.MS2019

The surrogate matrix methodology: A reference implementation for low-cost assembly in isogeometric analysis

Daniel Drzisga, Brendan Keith, Barbara Wohlmuth

A reference implementation of a new method in isogeometric analysis (IGA) is presented. It delivers low-cost variable-scale approximations (surrogates) of the matrices which IGA co…

cs.CE2019

Stencil scaling for vector-valued PDEs on hybrid grids with applications to generalized Newtonian fluids

Daniel Drzisga, Ulrich Rüde, Barbara Wohlmuth

Matrix-free finite element implementations for large applications provide an attractive alternative to standard sparse matrix data formats due to the significantly reduced memory c…