Estimation of Execution Parameters for k-Wave Simulations

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimation of Execution Parameters for k-Wave Simulations

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Estimation of Execution Parameters for k-Wave Simulations

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

High Performance Computing in Science and Engineering. HPCSE 2019

ISBN

978-3-030-67076-4

ISSN

—

e-ISSN

—

Počet stran výsledku

19

Strana od-do

116-134

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Hotel Soláň

Datum konání akce

20. 5. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—