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On April 11th, 2008, after my internal PhD defense has taken place, two of my jury members will give a talk. If you are interested, can join us in the Jozef Plateauzaal in the Faculty of Engineering building, Plateaustraat 22, in Ghent. The first talk will be given by Matthias Hauswirth (University of Lugano, Switzerland) and is titled Observer Effect and Measurement Bias in Performance Measurement. The second talk is by Kathryn McKinley (University of Texas at Austin, USA), is titled Dynamic Bug Detection for Managed Languages. The event starts at 14:30, and each talk will take about one hour of with 15 minutes are reserved for questions and such.

Observer Effect and Measurement Bias in Performance Measurement

To evaluate an innovation in computer systems, performance analysts measure execution time or other metrics using one or more standard workloads (e.g., the SPEC benchmarks). The performance analyst may carefully minimize the amount of measurement instrumentation, control the context in which measurement takes place, and repeat each meas-urement multiple times. Finally, the performance analyst may use the appropriate statistical techniques to characterize the data. Unfortunately, even with such a responsible approach, the collected data may or may not be actually useful. In this talk we show how easy it is to produce poor (and thus misleading) data in computer systems research. We explore two common sources of poor-quality data. First, we get poor-quality data if our data collection perturbs the behavior of the system that we are measuring; this is often known as the "observer effect". We show that even a seemingly insignificant measurement probe can dramatically alter system behavior; thus, perturbation is much more common than most performance analysts probably realize. Second, we get poor-quality data if we measure the system in a particular set of contexts and that set does not capture the range of reasonable contexts that a user of the system might encounter; this is known as "measurement bias". We show that different contexts favor different configurations of the system. We conclude our talk by outlining techniques for producing high-quality data.

Dynamic Bug Detection for Managed Languages

Although managed languages preclude and help prevent some software errors, deployed programs still have errors and crash. In this talk, we discuss approaches for detecting bugs and making deployed software more reliable. Our work focuses on efficient on-line techniques. We overview approaches for detecting the source of null pointer exceptions and efficiently computing calling context. We present an approach for detecting data structures that are growing. We show how to piggyback on the garbage collector to summarize efficiently (in time and space) the object volumes and relationships based on their user defined class. Experimental results show this approach is effective at finding memory leaks, i.e., data structure errors of omission. We include a brief discussion of in progress work on tolerating leaks. These results indicate promise for inexpensive approaches that help developers find bugs and protect users from their consequences.

You can find a PDF of the announcement here. For your convenience, I have put together a map.

Here are the short biographies of the speakers.

Matthias Hauswirth

He is an assistant professor at the University of Lugano in Switzerland. He is interested in approaches for measuring, understan-ding, and improving the performance of modern, complex systems. He is particularly intrigued by the intricate interactions between the different system layers, from the hardware, over the operating system, virtual machine, application frameworks, all the way to the applications.

Kathryn McKinley

She is a professor at the University of Texas. Her research interests include compiler optimization, architecture, memory management, and software engineering. She is currently serving as the Editor-and-Chief of TOPLAS and has been the program chair of ASPLOS, PACT and PLDI. She has graduated eight PhD students and is currently supervising eight graduate students.