Load Balancing For High Performance Computing Using Quantum Annealing
| dc.contributor.author | Rathore, Omer | |
| dc.contributor.author | Basden, Alastair | |
| dc.contributor.author | Chancellor, Nicholas | |
| dc.contributor.author | Kusumaatmaja, Halim | |
| dc.contributor.editor | Feichtinger, Kevin | |
| dc.contributor.editor | Sonnleithner, Lisa | |
| dc.contributor.editor | Hajiabadi, Hamideh | |
| dc.date.accessioned | 2025-02-14T10:03:35Z | |
| dc.date.available | 2025-02-14T10:03:35Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Load balancing is the distribution of computational work between available processors. Here, we investigate the application of quantum annealing to load balance two paradigmatic algorithms in high performance computing. Namely, adaptive mesh refinement and smoothed particle hydrodynamics are chosen as representative grid and off-grid target applications. While the methodology for obtaining real simulation data to partition is application specific, the proposed balancing protocol itself remains completely general. In a grid based context, quantum annealing is found to outperform classical methods such as the round robin protocol but lacks a decisive advantage over more advanced methods such as steepest descent or simulated annealing despite remaining competitive. However, for the more complex particle formulation, approached as a multi-objective optimization, quantum annealing solutions are demonstrably Pareto dominant to state of the art classical methods across both objectives. This signals a noteworthy advancement in solution quality which can have a large impact on effective CPU usage. | en |
| dc.identifier.doi | 10.18420/se2025-ws-20 | |
| dc.identifier.eissn | 2944-7682 | |
| dc.identifier.issn | 2944-7682 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/45829 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | Software Engineering 2025 – Companion Proceedings | |
| dc.subject | Quantum annealing | |
| dc.subject | Optimizing parallel applications | |
| dc.title | Load Balancing For High Performance Computing Using Quantum Annealing | en |
| mci.conference.date | 22.-28. Februar 2025 | |
| mci.conference.location | Karlsruhe | |
| mci.conference.sessiontitle | 2nd Quantum Software Engineering Meetup (QSE’25) | |
| mci.reference.pages | 215-216 |
Dateien
Originalbündel
1 - 1 von 1