Parallel Implementation of a Sensitivity Operator-Based Source Identification Algorithm for Distributed Memory Computers

Large-scale inverse problems that require high-performance computing arise in various fields, including regional air quality studies. The paper focuses on parallel solutions of an emission source identification problem for a 2D advection–diffusion–reaction model where the sources are identified by h...

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Veröffentlicht in:Mathematics (Basel) Jg. 10; H. 23; S. 4522
Hauptverfasser: Penenko, Alexey, Rusin, Evgeny
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.12.2022
Schlagworte:
adjoint equation
Air pollution
Air quality
Algorithms
Analysis
Data assimilation
Distributed memory
Distributed processing (Computers)
Emission analysis
Environmental impact
Food science
Identification
Inverse problems
Kalman filters
Lake Baikal region
large-scale inverse problem
Linear operators
Memory compaction
Memory management
Memory mapping
Memory partitioning
Memory protection
Memory refresh (Computers)
Message passing
Methods
Outdoor air quality
Parallel processing
Sensitivity
Sensitivity analysis
sensitivity operator
source identification
Russia
ISSN:2227-7390, 2227-7390
Online-Zugang:Volltext
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Zusammenfassung:Large-scale inverse problems that require high-performance computing arise in various fields, including regional air quality studies. The paper focuses on parallel solutions of an emission source identification problem for a 2D advection–diffusion–reaction model where the sources are identified by heterogeneous measurement data. In the inverse modeling approach we use, a source identification problem is transformed to a quasi-linear operator equation with a sensitivity operator, which allows working in a unified way with heterogeneous measurement data and provides natural parallelization of numeric algorithms by concurrent calculation of the rows of a sensitivity operator matrix. The parallel version of the algorithm implemented with a message passing interface (MPI) has shown a 40× speedup on four Intel Xeon Gold 6248R nodes in an inverse modeling scenario for the Lake Baikal region.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math10234522