Analysis of input-dependent program behavior using active profiling

  • Authors:
  • Xipeng Shen;Michael L. Scott;Chengliang Zhang;Sandhya Dwarkadas;Chen Ding;Mitsunori Ogihara

  • Affiliations:
  • College of William and Mary Williamsburg, VA;University of Rochester, Rochester, NY;University of Rochester, Rochester, NY;University of Rochester, Rochester, NY;University of Rochester, Rochester, NY;University of Rochester, Rochester, NY

  • Venue:
  • Proceedings of the 2007 workshop on Experimental computer science
  • Year:
  • 2007

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Abstract

Utility programs, which perform similar and largely independent operations on a sequence of inputs, include such common applications as compilers, interpreters, and document parsers; databases; and compression and encoding tools. The repetitive behavior of these programs, while often clear to users, has been difficult to capture automatically. We present an active profiling technique in which controlled inputs to utility programs are used to expose execution phases, which are then marked, automatically, through binary instrumentation, enabling us to exploit phase transitions in production runs with arbitrary inputs. We demonstrate the effectiveness and programmability of active profiling via experiments with six utility programs from the SPEC benchmark suite; compare to code and interval phases; and describe applications of active profiling to memory management and memory leak detection.