NDLI: VarEMU: an emulation testbed for variability-aware software

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VarEMU: an emulation testbed for variability-aware software

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VarEMU: an emulation testbed for variability-aware software

Content Provider	ACM Digital Library
Author	Wanner, Lucas Srivastava, Mani Elmalaki, Salma Gupta, Puneet Lai, Liangzhen
Abstract	Modern integrated circuits, fabricated in nanometer technologies, suffer from significant power/performance variation across-chip, chip-to-chip and over time due to aging and ambient fluctuations. Furthermore, several existing and emerging reliability loss mechanisms have caused increased transient, intermittent and permanent failure rates. While this variability has been typically addressed by process, device and circuit designers, there has been a recent push towards sensing and adapting to variability in the various layers of software. Current hardware platforms, however, typically lack variability sensing capabilities. Even if sensing capabilities were available, evaluating variability-aware software techniques across a significant number of hardware samples would prove exceedingly costly and time consuming. We introduce VarEMU, an extension to the QEMU virtual machine monitor that serves as a framework for the evaluation of variability-aware software techniques. VarEMU provides users with the means to emulate variations in power consumption and in fault characteristics and to sense and adapt to these variations in software. Through the use (and dynamic change) of parameters in a power model, users can create virtual machines that feature both static and dynamic variations in power consumption. Faults may be injected before or after, or completely replace the execution of any instruction. Power consumption and susceptibility to faults are also subject to dynamic change according to an aging model. A software stack for VarEMU features precise control over faults and provides virtual energy monitors to the operating system and processes. This allows users to precisely quantify and evaluate the effects of variations on individual applications. We show how VarEMU tracks energy consumption according to variation-aware power and aging models and give examples of how it may be used to quantify how faults in instruction execution affect applications.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781479914173
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-09-29
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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