Publication Details

Estimation of Execution Parameters for k-Wave Simulations

JAROŠ, M.; SASÁK, T.; TREEBY, B.; JAROŠ, J. Estimation of Execution Parameters for k-Wave Simulations. In High Performance Computing in Science and Engineering. HPCSE 2019. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer Nature Switzerland AG, 2021. p. 116-134. ISBN: 978-3-030-67076-4.
Czech title
Odhad parametrů spuštění k-Wave simulací
Type
conference paper
Language
English
Authors
Jaroš Marta, Ing., Ph.D. (DCSY)
Sasák Tomáš, Ing.
Treeby Bradley
Jaroš Jiří, doc. Ing., Ph.D. (DCSY)
URL
Keywords

Workflow management system, Performance data collection, Interpolation, Job scheduling, HPC as a service 

Abstract

Estimation of execution parameters takes centre stage in automatic offloading of complex biomedical workflows to cloud and high performance facilities. Since ordinary users have no or very limited knowledge of the performance characteristics of particular tasks in the workflow, the scheduling system has to have the capabilities to select appropriate amount of compute resources, e.g., compute nodes, GPUs, or processor cores and estimate the execution time and cost. The presented approach considers a fixed set of executables that can be used to create custom workflows, and collects performance data of successfully computed tasks. Since the workflows may differ in the structure and size of the input data, the execution parameters can only be obtained by searching the performance database and interpolating between similar tasks. This paper shows it is possible to predict the execution time and cost with a high confidence. If the task parameters are found in the performance database, the mean interpolation error stays below 2.29%. If only similar tasks are found, the mean interpolation error may grow up to 15%. Nevertheless, this is still an acceptable error since the cluster performance may vary on order of percent as well.

Published
2021
Pages
116–134
Proceedings
High Performance Computing in Science and Engineering. HPCSE 2019
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN
978-3-030-67076-4
Publisher
Springer Nature Switzerland AG
Place
Cham
DOI
10.1007/978-3-030-67077-1_7
EID Scopus
2-s2.0-85101322823
BibTeX 
@inproceedings{BUT168118,
  author="Marta {Jaroš} and Tomáš {Sasák} and Bradley {Treeby} and Jiří {Jaroš}",
  title="Estimation of Execution Parameters for k-Wave Simulations",
  booktitle="High Performance Computing in Science and Engineering. HPCSE 2019",
  year="2021",
  series="Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  pages="116--134",
  publisher="Springer Nature Switzerland AG",
  address="Cham",
  doi="10.1007/978-3-030-67077-1\{_}7",
  isbn="978-3-030-67076-4",
  url="https://link.springer.com/chapter/10.1007/978-3-030-67077-1_7"
}
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