TY - GEN
T1 - GLoop
T2 - 2017 Symposium on Cloud Computing, SoCC 2017
AU - Suzuki, Yusuke
AU - Yamada, Hiroshi
AU - Kato, Shinpei
AU - Kono, Kenji
N1 - Funding Information:
We acknowledge our shepherd Christopher J. Rossbach and the anonymous reviewers for their insightful comments. This work was supported in part by the Japan Society for the Promotion of Science (JSPS KAKENHI 15J09761) and Japan Science and Technology Agency (JST CREST JPMJCR1683).
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/9/24
Y1 - 2017/9/24
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 run-time 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 run-time 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=85032436190&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032436190&partnerID=8YFLogxK
U2 - 10.1145/3127479.3132023
DO - 10.1145/3127479.3132023
M3 - Conference contribution
AN - SCOPUS:85032436190
T3 - SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing
SP - 80
EP - 93
BT - SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing
PB - Association for Computing Machinery, Inc
Y2 - 24 September 2017 through 27 September 2017
ER -