NDLI: pVOCL: Power-Aware Dynamic Placement and Migration in Virtualized GPU Environments

Content Provider	IEEE Xplore Digital Library
Author	Lama, P. Yan Li Aji, A.M. Balaji, P. Dinan, J. Shucai Xiao Yunquan Zhang Wu-chun Feng Thakur, R. Xiaobo Zhou
Copyright Year	2013
Description	Author affiliation: Math. & Comp. Sci. Div., Argonne Nat. Lab., Argonne, IL, USA (Balaji, P.; Dinan, J.; Thakur, R.) \|\| Dept. of Comput. Sci., Univ. of Colorado, Colorado Springs, CO, USA (Lama, P.; Xiaobo Zhou) \|\| Dept. of Comput. Sci., Virginia Tech, Blacksburg, VA, USA (Aji, A.M.; Wu-chun Feng) \|\| Adv. Micro Devices, USA (Shucai Xiao) \|\| Inst. of Software, Beijing, China (Yan Li; Yunquan Zhang)
Abstract	Power-hungry Graphics processing unit (GPU) accelerators are ubiquitous in high performance computing data centers today. GPU virtualization frameworks introduce new opportunities for effective management of GPU resources by decoupling them from application execution. However, power management of GPU-enabled server clusters faces significant challenges. The underlying system infrastructure shows complex power consumption characteristics depending on the placement of GPU workloads across various compute nodes, power-phases and cabinets in a datacenter. GPU resources need to be scheduled dynamically in the face of time-varying resource demand and peak power constraints. We propose and develop a power-aware virtual OpenCL (pVOCL) framework that controls the peak power consumption and improves the energy efficiency of the underlying server system through dynamic consolidation and power-phase topology aware placement of GPU workloads. Experimental results show that pVOCL achieves significant energy savings compared to existing power management techniques for GPU-enabled server clusters, while incurring negligible impact on performance. It drives the system towards energy-efficient configurations by taking an optimal sequence of adaptation actions in a virtualized GPU environment and meanwhile keeps the power consumption below the peak power budget.
Starting Page	145
Ending Page	154
File Size	491988
Page Count	10
File Format	PDF
ISBN	9780769550008
ISSN	10636927
DOI	10.1109/ICDCS.2013.51
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphics processing units Power demand Topology Kernel Servers Monitoring Power measurement OpenCL GPU Accelerators Virtualization Power Management Dynamic Placement and Migration
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software

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