Argonne National Laboratory

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

TitlepVOCL: Power-Aware Dynamic Placement and Migration in Virtualized GPU Environments
Publication TypeConference Paper
Year of Publication2013
AuthorsLama, P, Li, Y, Aji, AM, Balaji, P, Dinan, J, Xiao, S, Zhang, Y, Feng, W, Thakur, R, Zhou, X
Conference Name33rd International Conference on Distributed Computing Systems
Conference LocationPhiladephia, PA
Other NumbersANL/MCS-P5048-1213

Power-hungry Graphics processing unit (GPU) ac- celerators 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. Ex- perimental 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.