Detail výsledku

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.
Typ
článek ve sborníku konference
Jazyk
anglicky
Autoři
Jaroš Marta, Ing., Ph.D., UPSY (FIT)
Sasák Tomáš, Ing., FIT (FIT)
Treeby Bradley
Jaroš Jiří, prof. Ing., Ph.D., UPSY (FIT)
Abstrakt

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.

Klíčová slova

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

URL
Rok
2021
Strany
116–134
Sborník
High Performance Computing in Science and Engineering. HPCSE 2019
Řada
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Konference
High Performance Computing in Science and Engineering 2019
ISBN
978-3-030-67076-4
Vydavatel
Springer Nature Switzerland AG
Místo
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"
}
Soubory
Projekty
Fotoakustická/ultrazvuková mamoskopie pro dektekci lézí v prsou, MŠMT, Společná technologická iniciativa ECSEL, PAMMOTH, zahájení: 2017-01-01, ukončení: 2021-06-30, ukončen
IT4Innovations excellence in science, MŠMT, Národní program udržitelnosti II, LQ1602, zahájení: 2016-01-01, ukončení: 2020-12-31, ukončen
Návrh, optimalizace a evaluace aplikačně specifických počítačových systémů, VUT, Vnitřní projekty VUT, FIT-S-20-6309, zahájení: 2020-03-01, ukončení: 2023-02-28, ukončen
Výzkumné skupiny
Výzkumná skupina Superpočítačových technologií SC@FIT (VZ SC@FIT)
Pracoviště
Ústav počítačových systémů (UPSY)
Nahoru