Paper Info
Title
Comparative Performance Modeling of Parallel Preconditioned Krylov Methods
Abstract
Many scientific and engineering computations rely on the scalable solution of large sparse linear systems. Preconditioned Krylov methods are widely used and offer many algorithmic choices whose performance varies depending on the characteristics of the linear system. In previous work, we have shown that the performance of different Krylov methods at small scales can be modeled using a small number of features based on structural and numerical properties of the input linear system. In this paper, we focus on comparing the scalability of parallel Krylov methods given different input properties without requiring extensive empirical measurements. We consider the PETSc implementations of Newton-Krylov methods to produce scalability rankings based on our new comparative modeling approach. The model-based ranking is validated by comparison with empirical results on a numerical simulation of driven fluid flow in a cavity.
Year
DOI
Venue
2017
10.1109/HPCC-SmartCity-DSS.2017.4
2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
Keywords
Field
DocType
comparative performance modeling,parallel preconditioned Krylov methods,scientific engineering computations,scalable solution,sparse linear systems,structural properties,numerical properties,input linear system,parallel Krylov methods,Newton-Krylov methods,scalability rankings,comparative modeling approach,input properties,PETSc implementations
Small number,Linear system,Ranking,Computer simulation,Computer science,Computational science,Fluid dynamics,Empirical measure,Computation,Scalability,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-2589-7
0
0.34
References 
Authors
7
3
Name
Order
Citations
PageRank
Kanika Sood100.34
Boyana Norris241739.46
Elizabeth R. Jessup337049.02