FAIR Sharing of Data in Autotuning Research (Vision Paper)

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F24%3A00139198" target="_blank" >RIV/00216224:14610/24:00139198 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3629527.3651429" target="_blank" >http://dx.doi.org/10.1145/3629527.3651429</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3629527.3651429" target="_blank" >10.1145/3629527.3651429</a>

Jazyk výsledku

angličtina

Název v původním jazyce

FAIR Sharing of Data in Autotuning Research (Vision Paper)

Popis výsledku v původním jazyce

Autotuning is an automated process that selects the best computer program implementation from a set of candidates to improve performance, such as execution time, when run under new circumstances, such as new hardware. The process of autotuning generates a large amount of performance data with multiple potential use cases, including reproducing results, comparing included methods, and understanding the impact of individual tuning parameters. We propose the adoption of FAIR Principles, which stands for Findable, Accessible, Interoperable, and Reusable, to organize the guidelines for data sharing in autotuning research. The guidelines aim to lessen the burden of sharing data and provide a comprehensive checklist of recommendations for shared data. We illustrate three examples that could greatly benefit from shared autotuning data to advance the research without time- and resource-demanding data collection.

Název v anglickém jazyce

FAIR Sharing of Data in Autotuning Research (Vision Paper)

Popis výsledku anglicky

Autotuning is an automated process that selects the best computer program implementation from a set of candidates to improve performance, such as execution time, when run under new circumstances, such as new hardware. The process of autotuning generates a large amount of performance data with multiple potential use cases, including reproducing results, comparing included methods, and understanding the impact of individual tuning parameters. We propose the adoption of FAIR Principles, which stands for Findable, Accessible, Interoperable, and Reusable, to organize the guidelines for data sharing in autotuning research. The guidelines aim to lessen the burden of sharing data and provide a comprehensive checklist of recommendations for shared data. We illustrate three examples that could greatly benefit from shared autotuning data to advance the research without time- and resource-demanding data collection.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/LM2018140" target="_blank" >LM2018140: e-Infrastruktura CZ</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

COMPANION OF THE 15TH ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING, ICPE COMPANION 2024

ISBN

9798400704451

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

21-27

Název nakladatele

ASSOC COMPUTING MACHINERY

Místo vydání

NEW YORK

Místo konání akce

London, ENGLAND

Datum konání akce

7. 5. 2024

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

001227617500004