scholarly journals Low/Adaptive Precision Computation in Preconditioned Iterative Solvers for Ill-Conditioned Problems

2022 ◽  
Author(s):  
Masatoshi Kawai ◽  
Kengo Nakajima
Minerals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 42 ◽  
Author(s):  
Nikolay Yavich ◽  
Mikhail Malovichko ◽  
Arseny Shlykov

This paper presents a novel numerical method for simulation controlled-source audio-magnetotellurics (CSAMT) and radio-magnetotellurics (CSRMT) data. These methods are widely used in mineral exploration. Interpretation of the CSAMT and CSRMT data collected over an area with the complex geology requires application of effective methods of numerical modeling capable to represent the geoelectrical model of a deposit well. In this paper, we considered an approach to 3D electromagnetic (EM) modeling based on new types of preconditioned iterative solvers for finite-difference (FD) EM simulation. The first preconditioner used fast direct inversion of the layered Earth FD matrix (Green’s function preconditioner). The other combined the first with a contraction operator transformation. To illustrate the effectiveness of the developed numerical modeling methods, a 3D resistivity model of Aleksandrovka study area in Kaluga Region, Russia, was prepared based on drilling data, AMT, and a detailed CSRMT survey. We conducted parallel EM simulation of the full CSRMT survey. Our results indicated that the developed methods can be effectively used for modeling EM responses over a realistic complex geoelectrical model for a controlled source EM survey with hundreds of receiver stations. The contraction-operator preconditioner outperformed the Green’s function preconditioner by factor of 7–10, both with respect to run-time and iteration count, and even more at higher frequencies.


Author(s):  
Arturo Rodriguez ◽  
V. M. Krushnarao Kotteda ◽  
Luis F. Rodriguez ◽  
Vinod Kumar ◽  
Jorge A. Munoz

Abstract MFiX is a multiphase open-source suite that is developed at the National Energy Technology Laboratories. It is widely used by fossil fuel reactor communities to simulate flow in a fluidized bed reactor. It does not have advanced linear iterative solvers even though it spends 70% of the run time in solving the linear system. Trilinos contains algorithms and enabling technologies for the solution of large-scale, sophisticated multi-physics engineering and scientific problems. The library developed at Sandia National Laboratories has more than 60 packages. It consists of state-of-the-art preconditioners, nonlinear solvers, direct solvers, and iterative solvers. The packages are performant and portable on various hybrid computing architectures. To improve the capabilities of MFiX, we developed a framework, MFiX-Trilinos, to integrate the advanced linear solvers in Trilinos with the FORTRAN based multiphase flow solver, MFiX. The framework changes the semantics of the array in FORTRAN and C++ and solve the linear system with packages in Trilinos and returns the solution to MFiX. The preconditioned iterative solvers considered for the analysis are BiCGStab and GMRES. The framework is verified on various fluidized bed problems. The performance of the framework is tested on the Stampede supercomputer. The wall time for multiple sizes of fluidized beds is compared.


2011 ◽  
Vol 68 (3) ◽  
pp. 269-286 ◽  
Author(s):  
S. A. Melchior ◽  
V. Legat ◽  
P. Van Dooren ◽  
A. J. Wathen

2018 ◽  
Vol 74 (8) ◽  
pp. 4104-4126 ◽  
Author(s):  
V. M. Krushnarao Kotteda ◽  
Vinod Kumar ◽  
William Spotz

Author(s):  
Giovanni Isotton ◽  
Carlo Janna ◽  
Massimo Bernaschi

The solution of linear systems of equations is a central task in a number of scientific and engineering applications. In many cases the solution of linear systems may take most of the simulation time thus representing a major bottleneck in the further development of scientific and technical software. For large scale simulations, nowadays accounting for several millions or even billions of unknowns, it is quite common to resort to preconditioned iterative solvers for exploiting their low memory requirements and, at least potential, parallelism. Approximate inverses have been shown to be robust and effective preconditioners in various contexts. In this work, we show how adaptive Factored Sparse Approximate Inverse (aFSAI), characterized by a very high degree of parallelism, can be successfully implemented on a distributed memory computer equipped with GPU accelerators. Taking advantage of GPUs in adaptive FSAI set-up is not a trivial task, nevertheless we show through an extensive numerical experimentation how the proposed approach outperforms more traditional preconditioners and results in a close-to-ideal behavior in challenging linear algebra problems.


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