Predicting the solvability space of the preconditioned krylov subspace methods

Shuting Xu, Sang Bae Kim, Jun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we predict the parameter solvability space of the preconditioned Krylov subspace methods with two or more parameters. The parameter solvability space is usually irregular, however, in many situations it shows spatial locality, i.e. the parameter locations that are closer in parameter space are more likely to have similar solvability. We propose three methods to predict the solvability of ILUT which make usage of spatial locality in different ways. The three methods are multi-points SVM classifier (MSC), overall SVM classifier (OSC), and Overall Spatial Autoregressive Classifier (OSAC). The experimental results show that both MSC and OSAC can obtain 90% accuracy in prediction, but OSAC is much simpler to implement. We focus our work on ILUT preconditioner [6], but the proposed strategies should be applicable to other preconditioners with two or more parameters. Copyright ISCA.

Original languageEnglish
Title of host publicationProceedings of the 31st International Conference on Computers and Their Applications, CATA 2016
EditorsAntoine Bossard
Pages293-297
Number of pages5
ISBN (Electronic)9781943436026
StatePublished - 2016
Event31st International Conference on Computers and Their Applications, CATA 2016 - Las Vegas, United States
Duration: Apr 4 2016Apr 6 2016

Publication series

NameProceedings of the 31st International Conference on Computers and Their Applications, CATA 2016

Conference

Conference31st International Conference on Computers and Their Applications, CATA 2016
Country/TerritoryUnited States
CityLas Vegas
Period4/4/164/6/16

ASJC Scopus subject areas

  • Computer Science Applications

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