Burger, Michael ; Nguyen, Giang Nam ; Bischof, Christian (2022)
SimAnMo — A parallelized runtime model generator.
In: Concurrency and Computation: Practice and Experience, 2022, 34 (20)
doi: 10.26083/tuprints-00022440
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (Abstract)
In this article, we present the novel features of the recent version of SimAnMo, the Simulated Annealing Modeler. The tool creates models that correlate the size of one input parameter of an application to the corresponding runtime and thus SimAnMo allows predictions for larger input sizes. A focus lies on applications whose runtime grows exponentially in the input parameter size. Such programs are, for example, of high interest for cryptanalysis to analyze practical security of traditional and post‐quantum secure schemes. However, SimAnMo also generates reliable models for the widespread case of polynomial runtime behavior and also for the important case of factorial runtime increase. SimAnMo's model generation is based on a parallelized simulated annealing procedure and heuristically minimizes the costs of a model. Those may rely on different quality metrics. Insights into SimAnMo's software design and its usage are provided. We demonstrate the quality of SimAnMo's models for different algorithms from various application fields. We show that our approach also works well on ARM architectures.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Burger, Michael ; Nguyen, Giang Nam ; Bischof, Christian |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | SimAnMo — A parallelized runtime model generator |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | John Wiley & Sons |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Concurrency and Computation: Practice and Experience |
| Jahrgang/Volume einer Zeitschrift: | 34 |
| (Heft-)Nummer: | 20 |
| Kollation: | 22 Seiten |
| DOI: | 10.26083/tuprints-00022440 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/22440 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | In this article, we present the novel features of the recent version of SimAnMo, the Simulated Annealing Modeler. The tool creates models that correlate the size of one input parameter of an application to the corresponding runtime and thus SimAnMo allows predictions for larger input sizes. A focus lies on applications whose runtime grows exponentially in the input parameter size. Such programs are, for example, of high interest for cryptanalysis to analyze practical security of traditional and post‐quantum secure schemes. However, SimAnMo also generates reliable models for the widespread case of polynomial runtime behavior and also for the important case of factorial runtime increase. SimAnMo's model generation is based on a parallelized simulated annealing procedure and heuristically minimizes the costs of a model. Those may rely on different quality metrics. Insights into SimAnMo's software design and its usage are provided. We demonstrate the quality of SimAnMo's models for different algorithms from various application fields. We show that our approach also works well on ARM architectures. |
| Freie Schlagworte: | exponential runtime, factorial runtime, runtime modeling, runtime prediction |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-224408 |
| Zusätzliche Informationen: | Special Issue: Performance Modeling, Benchmarking and Simulation of High-Performance Computing Systems (PMBS2020). International Conference on Innovations in Intelligent Systems and Applications (INISTA 2021). Recent advances in quantum computing and quantum neural networks |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Scientific Computing |
| Hinterlegungsdatum: | 07 Okt 2022 13:16 |
| Letzte Änderung: | 10 Okt 2022 12:42 |
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| Suche nach Titel in: | TUfind oder in Google |
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