• DocumentCode
    3076846
  • Title

    Analyzing the Impact of CPU Pinning and Partial CPU Loads on Performance and Energy Efficiency

  • Author

    Podzimek, Andrej ; Bulej, Lubomir ; Chen, Lydia Y. ; Binder, Walter ; Tuma, Petr

  • fYear
    2015
  • fDate
    4-7 May 2015
  • Firstpage
    1
  • Lastpage
    10
  • Abstract
    While workload collocation is a necessity to increase energy efficiency of contemporary multi-core hardware, it also increases the risk of performance anomalies due to workload interference. Pinning certain workloads to a subset of CPUs is a simple approach to increasing workload isolation, but its effect depends on workload type and system architecture. Apart from common sense guidelines, the effect of pinning has not been extensively studied so far. In this paper we study the impact of CPU pinning on performance interference and energy efficiency for pairs of collocated workloads. Besides various combinations of workloads, virtualization and resource isolation, we explore the effects of pinning depending on the level of background load. The presented results are based on more than 1000 experiments carried out on an Intel-based NUMA system, with all power management features enabled to reflect real-world settings. We find that less common CPU pinning configurations improve energy efficiency at partial background loads, indicating that systems hosting collocated workloads could benefit from dynamic CPU pinning based on CPU load and workload type.
  • Keywords
    performance evaluation; power aware computing; CPU pinning; Intel-based NUMA system; energy efficiency; partial CPU loads; performance interference; power management features; Benchmark testing; Hardware; Interference; Linux; Measurement; Servers; Throughput; KVM virtualization; LXC resource containers; feedback load controller; load throughput relation; partial CPU load; performance interference; reproducible load; workload colocation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on
  • Conference_Location
    Shenzhen
  • Type

    conf

  • DOI
    10.1109/CCGrid.2015.164
  • Filename
    7152466