FAIR Sharing of Data in Autotuning Research (Vision Paper)

D 2024

FAIR Sharing of Data in Autotuning Research (Vision Paper)

HOZZOVÁ, Jana; Jacob O TORRING; van Werkhoven BEN; David STŘELÁK; Richard VUDUC et. al.

Základní údaje

Originální název

FAIR Sharing of Data in Autotuning Research (Vision Paper)

Autoři

HOZZOVÁ, Jana (203 Česká republika, domácí); Jacob O TORRING; van Werkhoven BEN; David STŘELÁK (203 Česká republika, domácí) a Richard VUDUC

Vydání

NEW YORK, COMPANION OF THE 15TH ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING, ICPE COMPANION 2024, od s. 21-27, 7 s. 2024

Nakladatel

ASSOC COMPUTING MACHINERY

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14610/24:00139198

Organizační jednotka

Ústav výpočetní techniky

ISBN

979-8-4007-0445-1

DOI

http://dx.doi.org/10.1145/3629527.3651429

UT WoS

001227617500004

EID Scopus

2-s2.0-85193965903

Klíčová slova anglicky

autotuning; benchmarks; performance; measurements; open data; data sharing

Štítky

rivok

Příznaky

Mezinárodní význam

Změněno: 4. 4. 2025 13:14, Mgr. Eva Špillingová

Anotace

V originále

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.

Návaznosti

LM2018140, projekt VaV

Název: e-Infrastruktura CZ (Akronym: e-INFRA CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, e-Infrastruktura CZ

Citovat

HOZZOVÁ, Jana; Jacob O TORRING; van Werkhoven BEN; David STŘELÁK a Richard VUDUC. FAIR Sharing of Data in Autotuning Research (Vision Paper). Online. In COMPANION OF THE 15TH ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING, ICPE COMPANION 2024. NEW YORK: ASSOC COMPUTING MACHINERY, 2024, s. 21-27. ISBN 979-8-4007-0445-1. Dostupné z: https://dx.doi.org/10.1145/3629527.3651429.

@inproceedings{2487259,
   author = {Hozzová, Jana and Torring, Jacob O and Ben, van Werkhoven and Střelák, David and Vuduc, Richard},
   address = {NEW YORK},
   booktitle = {COMPANION OF THE 15TH ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING, ICPE COMPANION 2024},
   doi = {http://dx.doi.org/10.1145/3629527.3651429},
   keywords = {autotuning; benchmarks; performance; measurements; open data; data sharing},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {NEW YORK},
   isbn = {979-8-4007-0445-1},
   pages = {21-27},
   publisher = {ASSOC COMPUTING MACHINERY},
   title = {FAIR Sharing of Data in Autotuning Research (Vision Paper)},
   year = {2024}
}

TY  - CONF
ID  - 2487259
AU  - Hozzová, Jana - Torring, Jacob O - Ben, van Werkhoven - Střelák, David - Vuduc, Richard
PY  - 2024
TI  - FAIR Sharing of Data in Autotuning Research (Vision Paper)
PB  - ASSOC COMPUTING MACHINERY
CY  - NEW YORK
SN  - 9798400704451
KW  - autotuning
KW  - benchmarks
KW  - performance
KW  - measurements
KW  - open data
KW  - data sharing
N2  - 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.
ER  -

HOZZOVÁ, Jana; Jacob O TORRING; van Werkhoven BEN; David STŘELÁK a Richard VUDUC. FAIR Sharing of Data in Autotuning Research (Vision Paper). Online. In \textit{COMPANION OF THE 15TH ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING, ICPE COMPANION 2024}. NEW YORK: ASSOC COMPUTING MACHINERY, 2024, s.~21-27. ISBN~979-8-4007-0445-1. Dostupné z: https://dx.doi.org/10.1145/3629527.3651429.

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