Abstract
We consider a fast, data-sparse directional method to realize matrix-vector products related to point evaluations of the Helmholtz kernel. The method is based on a hierarchical partitioning of the point sets and the matrix. The considered directional multi-level approximation of the Helmholtz kernel can be applied even on high-frequency levels efficiently. We provide a detailed analysis of the almost linear asymptotic complexity of the presented method. Our numerical experiments are in good agreement with the provided theory.
Originalsprache | englisch |
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Titel | High Performance Computing in Science and Engineering - 4th International Conference, HPCSE 2019, Revised Selected Papers |
Untertitel | 4th International Conference, HPCSE 2019, Karolinka, Czech Republic, May 20–23, 2019, Revised Selected Papers |
Redakteure/-innen | Tomáš Kozubek, Peter Arbenz, Jiří Jaroš, Lubomír Říha, Jakub Šístek, Petr Tichý |
Herausgeber (Verlag) | Springer, Cham |
Seiten | 39-59 |
Seitenumfang | 21 |
ISBN (elektronisch) | 978-3-030-67077-1 |
ISBN (Print) | 978-3-030-67076-4 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2021 |
Veranstaltung | HPCSE 2019: High Performance Computing in Science and Engineering - Karolinka, Tschechische Republik Dauer: 20 Mai 2019 → 23 Mai 2019 http://hpcse.it4i.cz/HPCSE19/ |
Publikationsreihe
Name | Lecture Notes in Computer Science |
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Herausgeber (Verlag) | Springer |
Band | 12456 |
ISSN (Print) | 0302-9743 |
ISSN (elektronisch) | 1611-3349 |
Konferenz
Konferenz | HPCSE 2019 |
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Land/Gebiet | Tschechische Republik |
Ort | Karolinka |
Zeitraum | 20/05/19 → 23/05/19 |
Internetadresse |
ASJC Scopus subject areas
- Numerische Mathematik
Fields of Expertise
- Information, Communication & Computing