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Measuring the performance of interactive applications with listener latency profiling
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ACM International Conference Proceeding Series; Vol. 347 archive
Proceedings of the 6th international symposium on Principles and practice of programming in Java table of contents
Modena, Italy
SESSION: Optimization and run-time performance II table of contents
Pages 137-146  
Year of Publication: 2008
ISBN:978-1-60558-223-8
Authors
Milan Jovic  University of Lugano Lugano, Switzerland
Matthias Hauswirth  University of Lugano Lugano, Switzerland
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

When Java developers need to improve the performance of their applications, they usually use one of the many existing profilers for Java. These profilers generally capture a profile that represents the execution time spent in each method. The developer can thus focus her optimization efforts on the methods that consume the most time. In this paper we argue that this type of profile is insufficient for tuning interactive applications. Interactive applications respond to user events, such as mouse clicks and key presses. The perceived performance of interactive applications is directly related to the response time of the program.

In this paper we present listener latency profiling, a profiling approach with two distinctive characteristics. First, we call it latency profiling because it helps developers to find long latency operations. Second, we call it listener profiling because it abstracts away from method-level profiles to compute the latency of the various listeners. This allows a developer to reason about performance with respect to listeners, also called observers, the high level abstraction at the core of any interactive Java application.

We present our listener latency profiling approach, describe LiLa, our implementation, validate it on a set of micro-benchmarks, and evaluate it on a complex real-world interactive application.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Milan Jovic: colleagues
Matthias Hauswirth: colleagues