A benchmark set of highly-efficient CUDA and OpenCL kernels and
its dynamic autotuning with Kernel Tuning Toolkit

J 2020

A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit

PETROVIČ, Filip, David STŘELÁK, Jana HOZZOVÁ, Jaroslav OĽHA, Richard TREMBECKÝ et. al.

Základní údaje

Originální název

A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit

Název česky

Sada benchmarků vysoce efektivních CUDA a OpenCL kernelů a její dynamický autotuning za pomocí Kernel Tuning Toolkit

Autoři

PETROVIČ, Filip (703 Slovensko, domácí), David STŘELÁK (203 Česká republika, domácí), Jana HOZZOVÁ (703 Slovensko, domácí), Jaroslav OĽHA (703 Slovensko, domácí), Richard TREMBECKÝ (703 Slovensko, domácí), Siegfried BENKNER (40 Rakousko) a Jiří FILIPOVIČ (203 Česká republika, garant, domácí)

Vydání

Future Generation Computer Systems, Elsevier, 2020, 0167-739X

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 7.187

Kód RIV

RIV/00216224:14610/20:00115375

Organizační jednotka

Ústav výpočetní techniky

DOI

http://dx.doi.org/10.1016/j.future.2020.02.069

UT WoS

000528199900012

Klíčová slova česky

dynamický autotuning; OpenCL; CUDA; optimalizace výkonu; autotuning benchmarkovací sady

Klíčová slova anglicky

Dynamic autotuning; OpenCL; CUDA; Performance optimization; Autotuning benchmark set

Štítky

J-D1, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 25. 3. 2021 10:45, Mgr. Alena Mokrá

Anotace

V originále

In recent years, the heterogeneity of both commodity and supercomputers hardware has increased sharply. Accelerators, such as GPUs or Intel Xeon Phi co-processors, are often key to improving speed and energy efficiency of highly-parallel codes. However, due to the complexity of heterogeneous architectures, optimization of codes for a certain type of architecture as well as porting codes across different architectures, while maintaining a comparable level of performance, can be extremely challenging. Addressing the challenges associated with performance optimization and performance portability, autotuning has gained a lot of interest. Autotuning of performance-relevant source-code parameters allows to automatically tune applications without hard coding optimizations and thus helps with keeping the performance portable. In this paper, we introduce a benchmark set of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA. Using our Kernel Tuning Toolkit, we show that with autotuning most of the kernels reach near-peak performance on various GPUs and outperform baseline implementations on CPUs and Xeon Phis. Our evaluation also demonstrates that autotuning is key to performance portability. In addition to offline tuning, we also introduce dynamic autotuning of code optimization parameters during application runtime. With dynamic tuning, the Kernel Tuning Toolkit enables applications to re-tune performance-critical kernels at runtime whenever needed, for example, when input data changes. Although it is generally believed that autotuning spaces tend to be too large to be searched during application runtime, we show that it is not necessarily the case when tuning spaces are designed rationally. Many of our kernels reach near peak-performance with moderately sized tuning spaces that can be searched at runtime with acceptable overhead. Finally we demonstrate, how dynamic performance tuning can be integrated into a real-world application from cryo-electron microscopy domain.

Návaznosti

EF16_013/0001802, projekt VaV

Název: 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

MUNI/A/1076/2019, interní kód MU

Název: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 20 (Akronym: SKOMU)

Investor: Masarykova univerzita, Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 20, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

PETROVIČ, Filip, David STŘELÁK, Jana HOZZOVÁ, Jaroslav OĽHA, Richard TREMBECKÝ, Siegfried BENKNER a Jiří FILIPOVIČ. A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit. Future Generation Computer Systems. Elsevier, 2020, roč. 108, July, s. 161-177. ISSN 0167-739X. Dostupné z: https://dx.doi.org/10.1016/j.future.2020.02.069.

@article{1630456,
   author = {Petrovič, Filip and Střelák, David and Hozzová, Jana and Oľha, Jaroslav and Trembecký, Richard and Benkner, Siegfried and Filipovič, Jiří},
   article_number = {July},
   doi = {http://dx.doi.org/10.1016/j.future.2020.02.069},
   keywords = {Dynamic autotuning; OpenCL; CUDA; Performance optimization; Autotuning benchmark set},
   language = {eng},
   issn = {0167-739X},
   journal = {Future Generation Computer Systems},
   title = {A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit},
   url = {https://www.sciencedirect.com/science/article/pii/S0167739X19327360},
   volume = {108},
   year = {2020}
}

TY  - JOUR
ID  - 1630456
AU  - Petrovič, Filip - Střelák, David - Hozzová, Jana - Oľha, Jaroslav - Trembecký, Richard - Benkner, Siegfried - Filipovič, Jiří
PY  - 2020
TI  - A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit
JF  - Future Generation Computer Systems
VL  - 108
IS  - July
SP  - 161-177
EP  - 161-177
PB  - Elsevier
SN  - 0167739X
KW  - Dynamic autotuning
KW  - OpenCL
KW  - CUDA
KW  - Performance optimization
KW  - Autotuning benchmark set
UR  - https://www.sciencedirect.com/science/article/pii/S0167739X19327360
L2  - https://www.sciencedirect.com/science/article/pii/S0167739X19327360
N2  - In recent years, the heterogeneity of both commodity and supercomputers hardware has increased sharply. Accelerators, such as GPUs or Intel Xeon Phi co-processors, are often key to improving speed and energy efficiency of highly-parallel codes. However, due to the complexity of heterogeneous architectures, optimization of codes for a certain type of architecture as well as porting codes across different architectures, while maintaining a comparable level of performance, can be extremely challenging. Addressing the challenges associated with performance optimization and performance portability, autotuning has gained a lot of interest. Autotuning of performance-relevant source-code parameters allows to automatically tune applications without hard coding optimizations and thus helps with keeping the performance portable. In this paper, we introduce a benchmark set of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA. Using our Kernel Tuning Toolkit, we show that with autotuning most of the kernels reach near-peak performance on various GPUs and outperform baseline implementations on CPUs and Xeon Phis. Our evaluation also demonstrates that autotuning is key to performance portability. In addition to offline tuning, we also introduce dynamic autotuning of code optimization parameters during application runtime. With dynamic tuning, the Kernel Tuning Toolkit enables applications to re-tune performance-critical kernels at runtime whenever needed, for example, when input data changes. Although it is generally believed that autotuning spaces tend to be too large to be searched during application runtime, we show that it is not necessarily the case when tuning spaces are designed rationally. Many of our kernels reach near peak-performance with moderately sized tuning spaces that can be searched at runtime with acceptable overhead. Finally we demonstrate, how dynamic performance tuning can be integrated into a real-world application from cryo-electron microscopy domain.
ER  -

PETROVIČ, Filip, David STŘELÁK, Jana HOZZOVÁ, Jaroslav OĽHA, Richard TREMBECKÝ, Siegfried BENKNER a Jiří FILIPOVIČ. A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit. \textit{Future Generation Computer Systems}. Elsevier, 2020, roč.~108, July, s.~161-177. ISSN~0167-739X. Dostupné z: https://dx.doi.org/10.1016/j.future.2020.02.069.

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