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Efficient Ephemeris Models for Spacecraft Trajectory Simulations on GPUs

Schrammel, Fabian ; Renk, Florian ; Mazaheri, Arya ; Wolf, Felix (2020)
Efficient Ephemeris Models for Spacecraft Trajectory Simulations on GPUs.
26th International Conference on Parallel and Distributed Computing. Warsaw, Poland (22.-26.08.2022)
doi: 10.1007/978-3-030-57675-2_35
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

When a spacecraft is released into space, its initial condition and future trajectory in terms of position and speed cannot be precisely predicted. To ensure that the object does not violate space debris mitigation or planetary protection standards, such that it causes potential damage or contamination of celestial bodies, spacecraft-mission designers conduct a multitude of simulations to verify the validity of the set of all probable trajectories. Such simulations are usually independent from each other, making them a perfect match for parallelization. The European Space Agency (ESA) developed a GPU-based simulator for this purpose and achieved reasonable speedups in comparison with the established multi-threaded CPU version. However, we noticed that the performance starts to degrade as the spacecraft trajectories diverge in time. Our empirical analysis using GPU profilers showed that the application suffers from poor data locality and high memory traffic. In this paper, we propose an alternative data layout, which increases data locality within thread blocks. Furthermore, we introduce alternative model configurations that lower both algorithmic effort and the number of memory requests without violating accuracy requirements. Our experiments show that our method is able to accelerate the computations up to a factor of 2.6.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2020
Autor(en): Schrammel, Fabian ; Renk, Florian ; Mazaheri, Arya ; Wolf, Felix
Art des Eintrags: Bibliographie
Titel: Efficient Ephemeris Models for Spacecraft Trajectory Simulations on GPUs
Sprache: Englisch
Publikationsjahr: 18 August 2020
Verlag: Springer
Buchtitel: Euro-Par 2020: Parallel Processing
Reihe: Lecture Notes in Computer Science
Band einer Reihe: 12247
Veranstaltungstitel: 26th International Conference on Parallel and Distributed Computing
Veranstaltungsort: Warsaw, Poland
Veranstaltungsdatum: 22.-26.08.2022
DOI: 10.1007/978-3-030-57675-2_35
Kurzbeschreibung (Abstract):

When a spacecraft is released into space, its initial condition and future trajectory in terms of position and speed cannot be precisely predicted. To ensure that the object does not violate space debris mitigation or planetary protection standards, such that it causes potential damage or contamination of celestial bodies, spacecraft-mission designers conduct a multitude of simulations to verify the validity of the set of all probable trajectories. Such simulations are usually independent from each other, making them a perfect match for parallelization. The European Space Agency (ESA) developed a GPU-based simulator for this purpose and achieved reasonable speedups in comparison with the established multi-threaded CPU version. However, we noticed that the performance starts to degrade as the spacecraft trajectories diverge in time. Our empirical analysis using GPU profilers showed that the application suffers from poor data locality and high memory traffic. In this paper, we propose an alternative data layout, which increases data locality within thread blocks. Furthermore, we introduce alternative model configurations that lower both algorithmic effort and the number of memory requests without violating accuracy requirements. Our experiments show that our method is able to accelerate the computations up to a factor of 2.6.

Freie Schlagworte: LOEWE|SF4.0, DFG|320898076, LOEWE, DFG
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Parallele Programmierung
Zentrale Einrichtungen
Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ)
Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner
Hinterlegungsdatum: 04 Apr 2024 09:36
Letzte Änderung: 04 Jul 2024 04:40
PPN: 519507037
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