Vertical profiling: understanding the behavior of object-priented applications

  • Authors:

  • Matthias Hauswirth;Peter F. Sweeney;Amer Diwan;Michael Hind

  • Affiliations:

  • University of Colorado at Boulder;IBM Thomas J. Watson Research Center;University of Colorado at Boulder;IBM Thomas J. Watson Research Center

  • Venue:
  • OOPSLA '04 Proceedings of the 19th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
  • Year:
  • 2004

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Abstract

Object-oriented programming languages provide a rich set of features that provide significant software engineering benefits. The increased productivity provided by these features comes at a justifiable cost in a more sophisticated runtime system whose responsibility is to implement these features efficiently. However, the virtualization introduced by this sophistication provides a significant challenge to understanding complete system performance, not found in traditionally compiled languages, such as C or C++. Thus, understanding system performance of such a system requires profiling that spans all levels of the execution stack, such as the hardware, operating system, virtual machine, and application. In this work, we suggest an approach, called vertical profiling, that enables this level of understanding. We illustrate the efficacy of this approach by providing deep understandings of performance problems of Java applications run on a VM with vertical profiling support. By incorporating vertical profiling into a programming environment, the programmer will be able to understand how their program interacts with the underlying abstraction levels, such as application server, VM, operating system, and hardware.