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CHAPTER 8 THE MECHANICS OF EVENT FIRING
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poorly, because the Fire method must parse a parameter list or use other means to create the notification payload for an event whose type is determined on the fly at run time. Some studies have shown that in extreme cases, this overhead can represent a significant fraction of a system s activity.3 In such cases, optimizations might become necessary. While optimization of event-based systems is currently at the experimental level, a number of techniques have been studied and shown to be useful. In traditional systems, static analysis of call graphs can identify heavily used sections of code, providing a way to determine which methods should be optimized to give the greatest benefit. In an event-based system, a large number of bindings between objects is established at run time, so static tools are of little help. Optimization techniques with event-based systems must necessarily be dynamic and be based on the observation of the system while it is running. There is an irony in the performance analysis of component-based systems: The analysis may pinpoint problems that you can do little to correct, because the majority of optimizations often require modification of source code. When using third-party components, you often don t have access to the source code. Before any optimizations are done, you need to know what the event profile of the system is. An event profiler is a tool that essentially single-steps a system, much like a traditional debugger. Each time an event is fired, an entry is made in a profile log. The system is then run for a long enough time to build a statistically significant profile log. The log will then show the firing order of events in the system. You can then use tools to organize and visualize the event firing statistics, creating a toplevel profile of the system and identifying which events are most often fired, which event handlers are called, and other details. Profiling may identify not only high-frequency events, but also common event sequences that you might need to coalesce into shorter sequences. You may need to build different profiles if a system has different modes of operation. Once the profile of the system is known, you can apply static optimization techniques. Handlers called with the highest frequency can be recoded carefully to improve their speed. You can also apply compiler optimizations to inline code or store variables in CPU registers. Using a more dynamic technique, you can instrument the system with code that monitors the event firing patterns. When high-frequency events are detected, the associated code can be optimized on the fly through event-coalescing and other source code changes. The changes can then be compiled just-in-time and subsequently used.4 A number of experimental dynamic optimization systems have been proposed, including Tempo,5 Dynamo,6 and DynamoRIO.7
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3. Mohan Rajagopalan, Saumya K. Debray, Matti A. Hiltunen, and Richard D. Schlichting, Profile-Directed Optimization of Event-Based Programs (proceedings of the ACM Special Interest Group on Programming Languages [SIGPLAN], Berlin, Germany, June 2002). 4. Andreas Krall, Efficient JavaVM Just-in-Time Compilation (proceedings of the International Conference on Parallel Architectures and Compilation Techniques, Paris, France, October 1998). Matthew Arnold, Michael Hind, and Barbara Ryder, An Empirical Study of Adaptive Optimization (proceedings of the 13th International Workshop on Languages and Compilers for Parallel Computing, Yorktown Heights, New York, August 2000). 5. Charles Consel, Luke Hornof, Julia L. Lawall, Renaud Marlet, Gilles Muller, Jacques Noye, Scott Thibault, and Eugen-Nicolae Volanschi, Tempo: Specializing Systems Applications and Beyond, ACM Computing Surveys, Symposium on Partial Evaluation, September 1998. 6. Vasanth Bala, Evelyn Duesterwald, and Sanjeev Banerjia, Dynamo: A Transparent Dynamic Optimization System (proceedings of the SIGPLAN 2000 Conference on Programming Language Design and Implementation, Vancouver, British Columbia, Canada, June 2000). 7. Derek Bruening, Timothy Garnett, and Saman Amarasinghe, An Infrastructure for Adaptive Dynamic Optimization (proceedings of the 1st International Symposium on Code Generation and Optimization, San Francisco, CA, March 2003).
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