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Experimental Framework for Injecting Logic Errors in a Virtual Machine to Profile Applications for Soft Error Resilience

  • Nathan DeBardeleben
  • Sean Blanchard
  • Qiang Guan
  • Ziming Zhang
  • Song Fu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7156)

Abstract

As the high performance computing (HPC) community continues to push for ever larger machines, reliability remains a serious obstacle. Further, as feature size and voltages decrease, the rate of transient soft errors is on the rise. HPC programmers of today have to deal with these faults to a small degree and it is expected this will only be a larger problem as systems continue to scale.

In this paper we present SEFI, the Soft Error Fault Injection framework, a tool for profiling software for its susceptibility to soft errors. In particular, we focus in this paper on logic soft error injection. Using the open source virtual machine and processor emulator (QEMU), we demonstrate modifying emulated machine instructions to introduce soft errors. We conduct experiments by modifying the virtual machine itself in a way that does not require intimate knowledge of the tested application. With this technique, we show that we are able to inject simulated soft errors in the logic operations of a target application without affecting other applications or the operating system sharing the VM. We present some initial results and discuss where we think this work will be useful in next generation hardware/software co-design.

Keywords

soft errors resilience fault tolerance reliability fault injection virtual machines high performance computing supercomputing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nathan DeBardeleben
    • 1
  • Sean Blanchard
    • 1
  • Qiang Guan
    • 1
    • 2
  • Ziming Zhang
    • 1
    • 2
  • Song Fu
    • 2
  1. 1.High Performance Computing DivisionLos Alamos National Laboratory, Ultrascale Systems Research CenterLos AlamosUSA
  2. 2.Department of Computer Science and EngineeringUniversity of North Texas, Dependable Computing Systems LabDentonUSA

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