Cooperative GPGPU scheduling for consolidating server workloads

Yusuke Suzuki, Hiroshi Yamada, Shinpei Kato, Kenji Kono

Research output: Contribution to journalArticle

Abstract

Graphics processing units (GPUs) have become an attractive platform for general-purpose computing (GPGPU) in various domains. Making GPUs a time-multiplexing resource is a key to consolidating GPGPU applications (apps) in multi-tenant cloud platforms. However, advanced GPGPU apps pose a new challenge for consolidation. Such highly functional GPGPU apps, referred to as GPU eaters, can easily monopolize a shared GPU and starve collocated GPGPU apps. This paper presents GLoop, which is a software runtime that enables us to consolidate GPGPU apps including GPU eaters. GLoop offers an event-driven programming model, which allows GLoop-based apps to inherit the GPU eaters' high functionality while proportionally scheduling them on a shared GPU in an isolated manner. We implemented a prototype of GLoop and ported eight GPU eaters on it. The experimental results demonstrate that our prototype successfully schedules the consolidated GPGPU apps on the basis of its scheduling policy and isolates resources among them.

Original languageEnglish
Pages (from-to)3019-3037
Number of pages19
JournalIEICE Transactions on Information and Systems
VolumeE101D
Issue number12
DOIs
Publication statusPublished - 2018 Dec 1

Fingerprint

Servers
Scheduling
Graphics processing unit
Multiplexing
Consolidation

Keywords

  • Cloud computing
  • GPGPU
  • Operating systems

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Cite this

Cooperative GPGPU scheduling for consolidating server workloads. / Suzuki, Yusuke; Yamada, Hiroshi; Kato, Shinpei; Kono, Kenji.

In: IEICE Transactions on Information and Systems, Vol. E101D, No. 12, 01.12.2018, p. 3019-3037.

Research output: Contribution to journalArticle

Suzuki, Yusuke ; Yamada, Hiroshi ; Kato, Shinpei ; Kono, Kenji. / Cooperative GPGPU scheduling for consolidating server workloads. In: IEICE Transactions on Information and Systems. 2018 ; Vol. E101D, No. 12. pp. 3019-3037.
@article{1eb27d603766466292f211e6a4ec8802,
title = "Cooperative GPGPU scheduling for consolidating server workloads",
abstract = "Graphics processing units (GPUs) have become an attractive platform for general-purpose computing (GPGPU) in various domains. Making GPUs a time-multiplexing resource is a key to consolidating GPGPU applications (apps) in multi-tenant cloud platforms. However, advanced GPGPU apps pose a new challenge for consolidation. Such highly functional GPGPU apps, referred to as GPU eaters, can easily monopolize a shared GPU and starve collocated GPGPU apps. This paper presents GLoop, which is a software runtime that enables us to consolidate GPGPU apps including GPU eaters. GLoop offers an event-driven programming model, which allows GLoop-based apps to inherit the GPU eaters' high functionality while proportionally scheduling them on a shared GPU in an isolated manner. We implemented a prototype of GLoop and ported eight GPU eaters on it. The experimental results demonstrate that our prototype successfully schedules the consolidated GPGPU apps on the basis of its scheduling policy and isolates resources among them.",
keywords = "Cloud computing, GPGPU, Operating systems",
author = "Yusuke Suzuki and Hiroshi Yamada and Shinpei Kato and Kenji Kono",
year = "2018",
month = "12",
day = "1",
doi = "10.1587/transinf.2018EDP7027",
language = "English",
volume = "E101D",
pages = "3019--3037",
journal = "IEICE Transactions on Information and Systems",
issn = "0916-8532",
publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",
number = "12",

}

TY - JOUR

T1 - Cooperative GPGPU scheduling for consolidating server workloads

AU - Suzuki, Yusuke

AU - Yamada, Hiroshi

AU - Kato, Shinpei

AU - Kono, Kenji

PY - 2018/12/1

Y1 - 2018/12/1

N2 - Graphics processing units (GPUs) have become an attractive platform for general-purpose computing (GPGPU) in various domains. Making GPUs a time-multiplexing resource is a key to consolidating GPGPU applications (apps) in multi-tenant cloud platforms. However, advanced GPGPU apps pose a new challenge for consolidation. Such highly functional GPGPU apps, referred to as GPU eaters, can easily monopolize a shared GPU and starve collocated GPGPU apps. This paper presents GLoop, which is a software runtime that enables us to consolidate GPGPU apps including GPU eaters. GLoop offers an event-driven programming model, which allows GLoop-based apps to inherit the GPU eaters' high functionality while proportionally scheduling them on a shared GPU in an isolated manner. We implemented a prototype of GLoop and ported eight GPU eaters on it. The experimental results demonstrate that our prototype successfully schedules the consolidated GPGPU apps on the basis of its scheduling policy and isolates resources among them.

AB - Graphics processing units (GPUs) have become an attractive platform for general-purpose computing (GPGPU) in various domains. Making GPUs a time-multiplexing resource is a key to consolidating GPGPU applications (apps) in multi-tenant cloud platforms. However, advanced GPGPU apps pose a new challenge for consolidation. Such highly functional GPGPU apps, referred to as GPU eaters, can easily monopolize a shared GPU and starve collocated GPGPU apps. This paper presents GLoop, which is a software runtime that enables us to consolidate GPGPU apps including GPU eaters. GLoop offers an event-driven programming model, which allows GLoop-based apps to inherit the GPU eaters' high functionality while proportionally scheduling them on a shared GPU in an isolated manner. We implemented a prototype of GLoop and ported eight GPU eaters on it. The experimental results demonstrate that our prototype successfully schedules the consolidated GPGPU apps on the basis of its scheduling policy and isolates resources among them.

KW - Cloud computing

KW - GPGPU

KW - Operating systems

UR - http://www.scopus.com/inward/record.url?scp=85057548048&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85057548048&partnerID=8YFLogxK

U2 - 10.1587/transinf.2018EDP7027

DO - 10.1587/transinf.2018EDP7027

M3 - Article

AN - SCOPUS:85057548048

VL - E101D

SP - 3019

EP - 3037

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

SN - 0916-8532

IS - 12

ER -