HaPPy: Hyperthread-aware Power Profiling Dynamically Yan Zhai, University of Wisconsin; Xiao Zhang and Stephane Eranian, Google Inc.; Lingjia Tang and Jason Mars, University of Michigan https://www.usenix.org/conference/atc14/technical-sessions/presentation/zhai This paper is included in the Proceedings of USENIX ATC ’14: 2014 USENIX Annual Technical Conference. June 19–20, 2014 • Philadelphia, PA 978-1-931971-10-2 Open access to the Proceedings of USENIX ATC ’14: 2014 USENIX Annual Technical Conference is sponsored by USENIX. HaPPy: Hyperthread-aware Power Profiling Dynamically Yan Zhai Xiao Zhang, Stephane Eranian Lingjia Tang, Jason Mars University of Wisconsin Google Inc. University of Michigan
[email protected] xiaozhang,eranian @google.com lingjia,profmars @eesc.umich.edu { } { } Abstract specified power threshold by suspending a subset of jobs. Quantifying the power consumption of individual appli- Scheduling can also be used to limit processor utilization cations co-running on a single server is a critical compo- to reach energy consumption goals. Beyond power bud- nent for software-based power capping, scheduling, and geting, pricing the power consumed by jobs in datacen- provisioning techniques in modern datacenters. How- ters is also important in multi-tenant environments. ever, with the proliferation of hyperthreading in the last One capability that proves critical in enabling software few generations of server-grade processor designs, the to monitor and manage power resources in large-scale challenge of accurately and dynamically performing this datacenter infrastructures is the attribution of power con- power attribution to individual threads has been signifi- sumption to the individual applications co-running on cantly exacerbated.