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SimAnMo — A parallelized runtime model generator

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
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: SimAnMo — A parallelized runtime model generator
Language: English
Date: 7 October 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: John Wiley & Sons
Journal or Publication Title: Concurrency and Computation: Practice and Experience
Volume of the journal: 34
Issue Number: 20
Collation: 22 Seiten
DOI: 10.26083/tuprints-00022440
Corresponding Links:
Origin: Secondary publication DeepGreen

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.

Uncontrolled Keywords: exponential runtime, factorial runtime, runtime modeling, runtime prediction
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-224408
Additional Information:

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

Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > Scientific Computing
Date Deposited: 07 Oct 2022 13:16
Last Modified: 14 Nov 2023 19:05
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22440
PPN: 500225109
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