Estimation of Execution Parameters for k-Wave Simulations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU138609" target="_blank" >RIV/00216305:26230/21:PU138609 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-67077-1_7" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-67077-1_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-67077-1_7" target="_blank" >10.1007/978-3-030-67077-1_7</a>
Alternative languages
Result language
angličtina
Original language name
Estimation of Execution Parameters for k-Wave Simulations
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
High Performance Computing in Science and Engineering. HPCSE 2019
ISBN
978-3-030-67076-4
ISSN
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e-ISSN
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Number of pages
19
Pages from-to
116-134
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Hotel Soláň
Event date
May 20, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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