Navigator: Dynamic multi-kernel scheduling to improve GPU performance

Jiho Kim; John Kim; Yongjun Park

doi:10.1109/DAC18072.2020.9218711

Navigator: Dynamic multi-kernel scheduling to improve GPU performance

Jiho Kim, John Kim, Yongjun Park

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Efficient GPU resource-sharing between multiple kernels has recently been a critical factor on overall performance. While previous works mainly focused on how to allocate resources to two kernels, there has been limited amount of work on determining which workloads to concurrently execute among multiple workloads. Therefore, we first demonstrate on a real GPU system how the selection of concurrent workloads can have significant impact on overall performance. We then propose GPU Navigator - a lookup-table-based dynamic multi-kernel scheduler that maximizes overall performance through online profiling. Our evaluation shows that GPU Navigator outperforms a greedy policy by 29.3% on average.

Original language	English
Title of host publication	2020 57th ACM/IEEE Design Automation Conference, DAC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781450367257
DOIs	https://doi.org/10.1109/DAC18072.2020.9218711
Publication status	Published - 2020 Jul
Event	57th ACM/IEEE Design Automation Conference, DAC 2020 - Virtual, San Francisco, United States Duration: 2020 Jul 20 → 2020 Jul 24

Publication series

Name	Proceedings - Design Automation Conference
Volume	2020-July
ISSN (Print)	0738-100X

Conference

Conference	57th ACM/IEEE Design Automation Conference, DAC 2020
Country/Territory	United States
City	Virtual, San Francisco
Period	20/7/20 → 20/7/24

Bibliographical note

Funding Information:
We would like to thank to Jason Jong Kyu Park for his valuable feedback. This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP)(No.2018R1D1A1B07050609, No.2015M3C4A7065647, No.2020R1A2B5B01001687), ICT R&D program of MSIP/IITP (No.2017-0-00142), and the R&D program of MOTIE/KEIT (No.10077609). Yongjun Park is the corresponding author.

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering
Modelling and Simulation

Access to Document

10.1109/DAC18072.2020.9218711

Cite this

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title = "Navigator: Dynamic multi-kernel scheduling to improve GPU performance",

abstract = "Efficient GPU resource-sharing between multiple kernels has recently been a critical factor on overall performance. While previous works mainly focused on how to allocate resources to two kernels, there has been limited amount of work on determining which workloads to concurrently execute among multiple workloads. Therefore, we first demonstrate on a real GPU system how the selection of concurrent workloads can have significant impact on overall performance. We then propose GPU Navigator - a lookup-table-based dynamic multi-kernel scheduler that maximizes overall performance through online profiling. Our evaluation shows that GPU Navigator outperforms a greedy policy by 29.3% on average.",

author = "Jiho Kim and John Kim and Yongjun Park",

note = "Funding Information: We would like to thank to Jason Jong Kyu Park for his valuable feedback. This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP)(No.2018R1D1A1B07050609, No.2015M3C4A7065647, No.2020R1A2B5B01001687), ICT R&D program of MSIP/IITP (No.2017-0-00142), and the R&D program of MOTIE/KEIT (No.10077609). Yongjun Park is the corresponding author. Publisher Copyright: {\textcopyright} 2020 IEEE.; 57th ACM/IEEE Design Automation Conference, DAC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",

year = "2020",

month = jul,

doi = "10.1109/DAC18072.2020.9218711",

language = "English",

series = "Proceedings - Design Automation Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 57th ACM/IEEE Design Automation Conference, DAC 2020",

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Kim, J, Kim, J & Park, Y 2020, Navigator: Dynamic multi-kernel scheduling to improve GPU performance. in 2020 57th ACM/IEEE Design Automation Conference, DAC 2020., 9218711, Proceedings - Design Automation Conference, vol. 2020-July, Institute of Electrical and Electronics Engineers Inc., 57th ACM/IEEE Design Automation Conference, DAC 2020, Virtual, San Francisco, United States, 20/7/20. https://doi.org/10.1109/DAC18072.2020.9218711

Navigator: Dynamic multi-kernel scheduling to improve GPU performance. / Kim, Jiho; Kim, John; Park, Yongjun.
2020 57th ACM/IEEE Design Automation Conference, DAC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9218711 (Proceedings - Design Automation Conference; Vol. 2020-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: We would like to thank to Jason Jong Kyu Park for his valuable feedback. This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP)(No.2018R1D1A1B07050609, No.2015M3C4A7065647, No.2020R1A2B5B01001687), ICT R&D program of MSIP/IITP (No.2017-0-00142), and the R&D program of MOTIE/KEIT (No.10077609). Yongjun Park is the corresponding author. Publisher Copyright: © 2020 IEEE.

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AB - Efficient GPU resource-sharing between multiple kernels has recently been a critical factor on overall performance. While previous works mainly focused on how to allocate resources to two kernels, there has been limited amount of work on determining which workloads to concurrently execute among multiple workloads. Therefore, we first demonstrate on a real GPU system how the selection of concurrent workloads can have significant impact on overall performance. We then propose GPU Navigator - a lookup-table-based dynamic multi-kernel scheduler that maximizes overall performance through online profiling. Our evaluation shows that GPU Navigator outperforms a greedy policy by 29.3% on average.

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Navigator: Dynamic multi-kernel scheduling to improve GPU performance

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