Process variation characterization of chip-level multiprocessors

Zhang Lide*, Lan S. Bai, Robert P. Dick, Shang Li, Russell E Joseph

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations


Within-die variation in leakage power consumption is substantial and increasing for chip-level multiprocessors (CMPs) and multiprocessor systems-on-chip. Dealing with this problem via conservative assumptions is sub-optimal. Instead, operating systems may adapt task assignment and power management decisions to the variable characteristics of cores, improving system-wide power consumption and performance. Researchers have proposed such adaptation techniques. However, they rely on knowledge of CMP process variation (PV) maps. These maps are not provided by processor vendors, providing them would impose additional cost during the testing process, and static maps would not permit adaptation to aging effects. Further progress on developing and validating PV aware control techniques for CMPs requires access to PV maps for real processors. We present an online technique to extract the PV maps of CMPs. Potentially automatic temperature measurements with built-in on-die sensors during the execution of characterization workloads are used to determine variation in leakage power consumption. The proposed technique is applied to real CMPs, and the resulting PV maps are used within a PV aware task assignment and scheduling algorithm.

Original languageEnglish (US)
Title of host publication2009 46th ACM/IEEE Design Automation Conference, DAC 2009
Number of pages4
StatePublished - Nov 10 2009
Event2009 46th ACM/IEEE Design Automation Conference, DAC 2009 - San Francisco, CA, United States
Duration: Jul 26 2009Jul 31 2009


Other2009 46th ACM/IEEE Design Automation Conference, DAC 2009
Country/TerritoryUnited States
CitySan Francisco, CA


  • Characterization
  • Process variation
  • Software

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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