Application of reduce order modeling to time parallelization

Ashok Srinivasan, Yanan Yu, Namas Chandra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


We recently proposed a new approach to parallelization, by decomposing the time domain, instead of the conventional space domain. This improves latency tolerance, and we demonstrated its effectiveness in a practical application, where it scaled to much larger numbers of processors than conventional parallelization. This approach is fundamentally based on dynamically predicting the state of a system from data of related simulations. In earlier work, we used knowledge of the science of the problem to perform the prediction. In complicated simulations, this is not feasible. In this work, we show how reduced order modeling can be used for prediction, without requiring much knowledge of the science. We demonstrate its effectiveness in an important nano-materials application. The significance of this work lies in proposing a novel approach, based on established mathematical theory, that permits effective parallelization of time. This has important applications in multi-scale simulations, especially in dealing with long time-scales.

Original languageEnglish (US)
Title of host publicationHigh Performance Computing, HiPC 2005 - 12th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)3540309365, 9783540309369
StatePublished - 2005
Externally publishedYes
Event12th International Conference on High Performance Computing, HiPC 2005 - Goa, India
Duration: Dec 18 2005Dec 21 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3769 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th International Conference on High Performance Computing, HiPC 2005

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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