Exploiting historical data: Pruning autotuning spaces and
estimating the number of tuning steps

J 2020

Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps

OĽHA, Jaroslav, Jana HOZZOVÁ, Jan FOUSEK a Jiří FILIPOVIČ

Základní údaje

Originální název

Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps

Autoři

OĽHA, Jaroslav (703 Slovensko, garant, domácí), Jana HOZZOVÁ (703 Slovensko, domácí), Jan FOUSEK (203 Česká republika, domácí) a Jiří FILIPOVIČ (203 Česká republika, domácí)

Vydání

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, HOBOKEN, WILEY, 2020, 1532-0626

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 1.536

Kód RIV

RIV/00216224:14610/20:00116267

Organizační jednotka

Ústav výpočetní techniky

DOI

http://dx.doi.org/10.1002/cpe.5962

UT WoS

000557422400001

Klíčová slova anglicky

autotuning; prediction of tuning cost; sensitivity analysis; tuning space pruning

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 30. 8. 2022 14:20, Mgr. Michal Petr

Anotace

V originále

Autotuning, the practice of automatic tuning of applications to provide performance portability, has received increased attention in the research community, especially in high performance computing. Ensuring high performance on a variety of hardware usually means modifications to the code, often via different values of a selected set of parameters, such as tiling size, loop unrolling factor, or data layout. However, the search space of all possible combinations of these parameters can be large, which can result in cases where the benefits of autotuning are outweighed by its cost, especially with dynamic tuning. Therefore, estimating the tuning time in advance or shortening the tuning time is very important in dynamic tuning applications. We have found that certain properties of tuning spaces do not vary much when hardware is changed. In this article, we demonstrate that it is possible to use historical data to reliably predict the number of tuning steps that is necessary to find a well-performing configuration and to reduce the size of the tuning space. We evaluate our hypotheses on a number of HPC benchmarks written in CUDA and OpenCL, using several different generations of GPUs and CPUs.

Návaznosti

EF16_013/0001802, projekt VaV

Název: CERIT Scientific Cloud

LM2015085, projekt VaV

Název: CERIT Scientific Cloud (Akronym: CERIT-SC)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, CERIT Scientific Cloud

MUNI/A/1050/2019, interní kód MU

Název: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IX (Akronym: SV-FI MAV IX)

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IX, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

OĽHA, Jaroslav, Jana HOZZOVÁ, Jan FOUSEK a Jiří FILIPOVIČ. Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE. HOBOKEN: WILEY, 2020, roč. 32, č. 21, s. 1-15. ISSN 1532-0626. Dostupné z: https://dx.doi.org/10.1002/cpe.5962.

@article{1675386,
   author = {Oľha, Jaroslav and Hozzová, Jana and Fousek, Jan and Filipovič, Jiří},
   article_location = {HOBOKEN},
   article_number = {21},
   doi = {http://dx.doi.org/10.1002/cpe.5962},
   keywords = {autotuning; prediction of tuning cost; sensitivity analysis; tuning space pruning},
   language = {eng},
   issn = {1532-0626},
   journal = {CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE},
   title = {Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps},
   url = {https://onlinelibrary.wiley.com/doi/10.1002/cpe.5962},
   volume = {32},
   year = {2020}
}

TY  - JOUR
ID  - 1675386
AU  - Oľha, Jaroslav - Hozzová, Jana - Fousek, Jan - Filipovič, Jiří
PY  - 2020
TI  - Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps
JF  - CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
VL  - 32
IS  - 21
SP  - 1-15
EP  - 1-15
PB  - WILEY
SN  - 15320626
KW  - autotuning
KW  - prediction of tuning cost
KW  - sensitivity analysis
KW  - tuning space pruning
UR  - https://onlinelibrary.wiley.com/doi/10.1002/cpe.5962
L2  - https://onlinelibrary.wiley.com/doi/10.1002/cpe.5962
N2  - Autotuning, the practice of automatic tuning of applications to provide performance portability, has received increased attention in the research community, especially in high performance computing. Ensuring high performance on a variety of hardware usually means modifications to the code, often via different values of a selected set of parameters, such as tiling size, loop unrolling factor, or data layout. However, the search space of all possible combinations of these parameters can be large, which can result in cases where the benefits of autotuning are outweighed by its cost, especially with dynamic tuning. Therefore, estimating the tuning time in advance or shortening the tuning time is very important in dynamic tuning applications. We have found that certain properties of tuning spaces do not vary much when hardware is changed. In this article, we demonstrate that it is possible to use historical data to reliably predict the number of tuning steps that is necessary to find a well-performing configuration and to reduce the size of the tuning space. We evaluate our hypotheses on a number of HPC benchmarks written in CUDA and OpenCL, using several different generations of GPUs and CPUs.
ER  -

OĽHA, Jaroslav, Jana HOZZOVÁ, Jan FOUSEK a Jiří FILIPOVIČ. Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps. \textit{CONCURRENCY AND COMPUTATION-PRACTICE \&{} EXPERIENCE}. HOBOKEN: WILEY, 2020, roč.~32, č.~21, s.~1-15. ISSN~1532-0626. Dostupné z: https://dx.doi.org/10.1002/cpe.5962.

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