Abstract
Programs encounter increasingly complex and fragile mappings to computing platforms, resulting in performance characteristics that are often mysterious to students, practitioners, and even researchers. We discuss some steps toward an experimental methodology that demands and provides a deep understanding of complete systems, the necessary instrumentation and tools to support such a methodology, and a curriculum that teaches the methodology and tools as a fundamental part of the discipline.
- David F. Bacon, Perry Cheng, and David Grove. TuningFork: a platform for visualization and analysis of complex real-time systems. In OOPSLA 2007 Conference Companion, pages 854--855, Montreal, Quebec, 2007. Google Scholar
Digital Library
- Gregor Kiczales. Beyond the black box: Open implementation. IEEE Software, 13(1):8--11, 1996. Google Scholar
Digital Library
- Edward A. Lee. The problem with threads. IEEE Computer, 39(5):33--42, 2006. Google Scholar
Digital Library
- Todd Mytkowicz, Peter F. Sweeney, Matthias Hauswirth, and Amer Diwan. Observer effect and measurement bias in performance measurement. Submitted for publication, 2008.Google Scholar
- Hebert A. Simon. The Sciences of the Artificial. MIT Press, third edition, 1996. Originally published in 1969. Google Scholar
Digital Library
- TuningFork Visualization Platform. http://tuningforkvp.sourceforge.net.Google Scholar
Index Terms
Languages and performance engineering: method, instrumentation, and pedagogy
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