Parallelization of Hierarchical Matrix Algorithms for Electromagnetic Scattering Problems
| Autor: | Larsson, E., Zafari, A., Righero, M., Francavilla, M.A., Giordanengo, G., Vipiana, F., Vecchi, G., Kessler, C., Ancourt, C., Grelck, C., Kołodziej, J., González-Vélez, H. |
|---|---|
| Přispěvatelé: | Uppsala University, Department of Information Technology (DIT-UPPSALA), Antenna and Electromagnetic Compatibility Laboratory (Politecnico di Torino) (LACE), ASML Netherlands BV, De Run 6501, NL-5504 DR Veldhoven, Netherlands, Department of Electronics and Telecommunications [Torino] (DET), Politecnico di Torino = Polytechnic of Turin (Polito), Department of Computer and Information Science - Linköping University, Linköping University (LIU), Centre de Recherche en Informatique (CRI), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Paris sciences et lettres (PSL), ISLA - Informatics Institute (UNIVERSITY OF AMSTERDAM), University of Amsterdam [Amsterdam] (UvA), Parallel Computing Systems (IvI, FNWI) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
050101 languages & linguistics
Computer science Fast multipole method Electromagnetic Scattering [INFO.PAR]Computer Science [cs]/domain_info.par 02 engineering and technology Parallel computing System of linear equations Matrix (mathematics) 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Representation (mathematics) Hierarchical matrix Task parallel 05 social sciences Computational mathematics Binary logarithm Nested equivalent source approximation [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism Direct methods [INFO.SOC]Computer Science [cs]/domain_info.soc 020201 artificial intelligence & image processing [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] Algorithm |
| Zdroj: | High-Performance Modelling and Simulation for Big Data Applications High-Performance Modelling and Simulation for Big Data Applications, pp.36-68, 2019, 978-3-030-16272-6. ⟨10.1007/978-3-030-16272-6_2⟩ High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet, 36-68 STARTPAGE=36;ENDPAGE=68;TITLE=High-Performance Modelling and Simulation for Big Data Applications Lecture Notes in Computer Science ISBN: 9783030162719 |
| ISSN: | 0302-9743 |
| DOI: | 10.1007/978-3-030-16272-6_2 |
| Popis: | International audience; Numerical solution methods for electromagnetic scattering problems lead to large systems of equations with millions or even billions of unknown variables. The coefficient matrices are dense, leading to large computational costs and storage requirements if direct methods are used. A commonly used technique is to instead form a hierarchical representation for the parts of the matrix that corresponds to far-field interactions. The overall computational cost and storage requirements can then be reduced to O(N log N). This still corresponds to a large-scale simulation that requires parallel implementation. The hierarchical algorithms are rather complex, both regarding data dependencies and communication patterns, making parallelization non-trivial. In this chapter, we describe two classes of algorithms in some detail, we provide a survey of existing solutions, we show results for a proof-of-concept implementation, and we provide various perspectives on different aspects of the problem. The list of authors is organized into three subgroups, Larsson and Zafari (coordination and proof-of-concept implementation), Righero, Francavilla, Giordanengo, Vipiana, and Vecchi (definition of and expertise relating to the application), Kessler, Ancourt, and Grelck (perspectives and parallel expertise). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|