Performance and scalability of the block low-rank multifrontal factorization on multicore architectures
PR Amestoy, A Buttari, JY L'excellent… - ACM Transactions on …, 2019 - dl.acm.org
Matrices coming from elliptic Partial Differential Equations have been shown to have a low-
rank property that can be efficiently exploited in multifrontal solvers to provide a substantial …
rank property that can be efficiently exploited in multifrontal solvers to provide a substantial …
Fast 3D frequency-domain full-waveform inversion with a parallel block low-rank multifrontal direct solver: Application to OBC data from the North Sea
Wide-azimuth long-offset ocean bottom cable (OBC)/ocean bottom node surveys provide a
suitable framework to perform computationally efficient frequency-domain full-waveform …
suitable framework to perform computationally efficient frequency-domain full-waveform …
Parallel approximation of the maximum likelihood estimation for the prediction of large-scale geostatistics simulations
Maximum likelihood estimation is an important statistical technique for estimating missing
data, for example in climate and environmental applications, which are usually large and …
data, for example in climate and environmental applications, which are usually large and …
Sparse supernodal solver using block low-rank compression: Design, performance and analysis
This paper presents two approaches using a Block Low-Rank (BLR) compression technique
to reduce the memory footprint and/or the time-to-solution of the sparse supernodal solver …
to reduce the memory footprint and/or the time-to-solution of the sparse supernodal solver …
Bridging the gap between flat and hierarchical low-rank matrix formats: The multilevel block low-rank format
Matrices possessing a low-rank property arise in numerous scientific applications. This
property can be exploited to provide a substantial reduction of the complexity of their LU or …
property can be exploited to provide a substantial reduction of the complexity of their LU or …
Fast multipole method as a matrix-free hierarchical low-rank approximation
There has been a large increase in the amount of work on hierarchical low-rank
approximation methods, where the interest is shared by multiple communities that previously …
approximation methods, where the interest is shared by multiple communities that previously …
A Preconditioning Approach for the Domain Decomposition Simulation of High-Speed Circuits
J Lu - IEEE Transactions on Microwave Theory and …, 2023 - ieeexplore.ieee.org
Domain decomposition methods (DDMs) provide a powerful discretization framework for
analyzing the electromagnetic (EM) phenomena in complex high-speed circuits. However …
analyzing the electromagnetic (EM) phenomena in complex high-speed circuits. However …
``Compress and eliminate” solver for symmetric positive definite sparse matrices
DA Sushnikova, IV Oseledets - SIAM Journal on Scientific Computing, 2018 - SIAM
We propose a new approximate factorization for solving linear systems with symmetric
positive definite sparse matrices. In a nutshell the algorithm applies hierarchically block …
positive definite sparse matrices. In a nutshell the algorithm applies hierarchically block …
Sparse hierarchical solvers with guaranteed convergence
Solving sparse linear systems from discretized partial differential equations is challenging.
Direct solvers have, in many cases, quadratic complexity (depending on geometry), while …
Direct solvers have, in many cases, quadratic complexity (depending on geometry), while …
Sparse supernodal solver using block low-rank compression
This paper presents two approaches using a Block Low-Rank (BLR) compression technique
to reduce the memory footprint and/or the time-to-solution of the sparse supernodal solver …
to reduce the memory footprint and/or the time-to-solution of the sparse supernodal solver …