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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| Vydáno v: | Mathematics (Basel) Ročník 10; číslo 23; s. 4522 |
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| Jazyk: | angličtina |
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MDPI AG
01.12.2022
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| ISSN: | 2227-7390, 2227-7390 |
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| Abstract | 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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| AbstractList | 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. |
| Audience | Academic |
| Author | Penenko, Alexey Rusin, Evgeny |
| Author_xml | – sequence: 1 givenname: Alexey orcidid: 0000-0002-1729-3343 surname: Penenko fullname: Penenko, Alexey – sequence: 2 givenname: Evgeny orcidid: 0000-0001-8243-5793 surname: Rusin fullname: Rusin, Evgeny |
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| SubjectTerms | 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 |
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| Title | Parallel Implementation of a Sensitivity Operator-Based Source Identification Algorithm for Distributed Memory Computers |
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