Victor A. Paludetto Magri

Victor A. Paludetto Magri
Lawrence Livermore National Laboratory | LLNL · Center for Applied Scientific Computing

Ph.D.

About

8
Publications
1,061
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53
Citations
Additional affiliations
October 2015 - October 2018
University of Padova
Position
  • PhD Student

Publications

Publications (8)
Article
The numerical simulation of structural mechanics applications via finite elements usually requires the solution of large-size linear systems, especially when accurate results are sought for derived variables, like stress or deformation fields. Such a task represents the most time-consuming kernel, and motivates the development of robust and efficie...
Preprint
Full-text available
The numerical simulation of structural mechanics applications via finite elements usually requires the solution of large-size and ill-conditioned linear systems, especially when accurate results are sought for derived variables interpolated with lower order functions, like stress or deformation fields. Such task represents the most time-consuming k...
Preprint
Full-text available
The numerical simulation of structural mechanics applications via finite elements usually requires the solution of large-size and ill-conditioned linear systems, especially when accurate results are sought for derived variables interpolated with lower order functions, like stress or deformation fields. Such task represents the most time-consuming k...
Article
The numerical simulation of modern engineering problems can easily incorporate millions or even billions degrees of freedom. In several applications, these simulations require the solution to sparse linear systems of equations, and algebraic multigrid (AMG) methods are often standard choices as iterative solvers or preconditioners. This happens due...
Article
Full-text available
The use of factorized sparse approximate inverse (FSAI) preconditioners in a standard multilevel framework for symmetric positive definite (SPD) matrices may pose a number of issues as to the definiteness of the Schur complement at each level. The present work introduces a robust multilevel approach for SPD problems based on FSAI preconditioning, w...
Article
Factorized sparse approximate inverse (FSAI) preconditioners are robust algorithms for symmetric positive matrices, which are particularly attractive in a parallel computational environment because of their inherent and almost perfect scalability. Their parallel degree is even redundant with respect to the actual capabilities of the current computa...
Conference Paper
A numerical package called M3E_LINSOL for the solution of large linear systems of equations arising from reservoir simulations is presented. This suite includes Krylov-based solvers combined with a set of Factorized Sparse Approximate Inverse (FSAI) preconditioners specifically designed for massively parallel architectures. The computational effici...

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Projects

Projects (2)
Archived project
The main goal of the project is incorporating Adaptive FSAI as an advanced smoother in AMG to address real world problems such as those in mechanics, coupled consolidation and reservoir simulation. The key point of the project is finding coarsening and prolongation strategies capable to equilibrate the smoother deficiency.