Article

DYNAMIC DATA-DRIVEN INVERSION FOR TERASCALE SIMULATIONS: REAL-TIME IDENTIFICATION OF AIRBORNE CONTAMINANTS

Authors:

VOLKAN AKCELIK,

ANDREI DRAGANESCU,

BART VAN BLOEMEN WAANDERSAuthors Info & Claims

SC '05: Proceedings of the 2005 ACM/IEEE conference on Supercomputing

Page 43

https://doi.org/10.1109/SC.2005.25

Published: 12 November 2005 Publication History

Abstract

In contrast to traditional terascale simulations that have known, fixed data inputs, dynamic data-driven (DDD) applications are characterized by unknown data and informed by dynamic observations. DDD simulations give rise to inverse problems of determining unknown data from sparse observations. The main difficulty is that the optimality system is a boundary value problem in 4D space-time, even though the forward simulation is an initial value problem. We construct special-purpose parallel multigrid algorithms that exploit the spectral structure of the inverse operator. Experiments on problems of localizing airborne contaminant release from sparse observations ina regional atmospheric transport model demonstrate that 17-million-parameter inversion can be effected at a cost of just 18 forward simulations with high parallel efficiency. On 1024 Alphaserver EV68 processors, the turnaround time is just 29 minutes. Moreover, inverse problems with 135 million parameters - corresponding to 139 billion total space-time unknowns - are solved in less than 5 hours on the same number of processors. These results suggest that ultra-high resolution data-driven inversion can be carried out sufficiently rapidly forsimulation-based "real-time" hazard assessment.

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Cited By

Hückelheim JSchanen MNarayanan SHovland P(2020)Vector Forward Mode Automatic Differentiation on SIMD/SIMT architecturesProceedings of the 49th International Conference on Parallel Processing10.1145/3404397.3404470(1-11)Online publication date: 17-Aug-2020
https://dl.acm.org/doi/10.1145/3404397.3404470
Saibaba AAlexanderian AIpsen I(2017)Randomized matrix-free trace and log-determinant estimatorsNumerische Mathematik10.1007/s00211-017-0880-z137:2(353-395)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s00211-017-0880-z
Deng XCai XZou J(2016)Two-Level Space---Time Domain Decomposition Methods for Three-Dimensional Unsteady Inverse Source ProblemsJournal of Scientific Computing10.1007/s10915-015-0109-167:3(860-882)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s10915-015-0109-1
Show More Cited By

Index Terms

DYNAMIC DATA-DRIVEN INVERSION FOR TERASCALE SIMULATIONS: REAL-TIME IDENTIFICATION OF AIRBORNE CONTAMINANTS
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation evaluation
    2. Simulation support systems

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cover image ACM Conferences

SC '05: Proceedings of the 2005 ACM/IEEE conference on Supercomputing

November 2005

829 pages

ISBN:1595930612

General Chair:
William Kramer

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SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

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IEEE Computer Society

United States

Publication History

Published: 12 November 2005

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SC '05

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SIGARCH
IEEE-CS

SC '05: International Conference for High Performance Computing, Networking, Storage and Analysis

November 12 - 18, 2005

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SC '05 Paper Acceptance Rate 62 of 260 submissions, 24%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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Cited By

Hückelheim JSchanen MNarayanan SHovland P(2020)Vector Forward Mode Automatic Differentiation on SIMD/SIMT architecturesProceedings of the 49th International Conference on Parallel Processing10.1145/3404397.3404470(1-11)Online publication date: 17-Aug-2020
https://dl.acm.org/doi/10.1145/3404397.3404470
Saibaba AAlexanderian AIpsen I(2017)Randomized matrix-free trace and log-determinant estimatorsNumerische Mathematik10.1007/s00211-017-0880-z137:2(353-395)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s00211-017-0880-z
Deng XCai XZou J(2016)Two-Level Space---Time Domain Decomposition Methods for Three-Dimensional Unsteady Inverse Source ProblemsJournal of Scientific Computing10.1007/s10915-015-0109-167:3(860-882)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s10915-015-0109-1
Peng LSilic MMohseni K(2015)A DDDAS Plume Monitoring System with Reduced Kalman Filter1Procedia Computer Science10.1016/j.procs.2015.05.36151:C(2533-2542)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.procs.2015.05.361
Bloemen Waanders BCarnes B(2011)Optimization under adaptive error control for finite element based simulationsComputational Mechanics10.1007/s00466-010-0530-047:1(49-63)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.1007/s00466-010-0530-0
Bashir OGhattas OHill JBloemen Waanders BWillcox K(2007)Hessian-Based Model Reduction for Large-Scale Data Assimilation ProblemsProceedings of the 7th international conference on Computational Science, Part I: ICCS 200710.1007/978-3-540-72584-8_133(1010-1017)Online publication date: 27-May-2007
https://dl.acm.org/doi/10.1007/978-3-540-72584-8_133

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