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"Vertical Profiling: Understanding the Behavior of Object-Oriented Applications"
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Vertical Profiling: Understanding the Behavior of Object-Oriented Applications
Ballroom A-B
Wednesday, 15:30, 30 minutes | 7 | · | 8 | · | 9 | · | 10 | · | 11 | · | 12 | · | 13 | · | 14 | · | 15 | · | 16 | · | 17 | · | 18 | · | 19 | · | 20 | · | 21 |
Matthias Hauswirth, University of Colorado at Boulder
Peter Sweeney, IBM Thomas J. Watson Research Center
Amer Diwan, University of Colorado at Boulder
Michael Hind, IBM Thomas J. Watson Research Center
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 a 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 an 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.
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