Languages and performance engineering: method, instrumentation, and pedagogy

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.

REFERENCES

• 1: David F. Bacon , Perry Cheng , David Grove, TuningFork: a platform for visualization and analysis of complex real-time systems, Companion to the 22nd ACM SIGPLAN conference on Object-oriented programming systems and applications companion, October 21-25, 2007, Montreal, Quebec, Canada [doi>10.1145/1297846.1297923]
• 2: Gregor Kiczales, Beyond the Black Box: Open Implementation, IEEE Software, v.13 n.1, p.8-11, January 1996
• 3: Edward A. Lee, The Problem with Threads, Computer, v.39 n.5, p.33-42, May 2006 [doi>10.1109/MC.2006.180]
• 4: Todd Mytkowicz, Peter F. Sweeney, Matthias Hauswirth, and Amer Diwan. Observer effect and measurement bias in performance measurement. Submitted for publication, 2008.
• 5: Herbert A. Simon, The sciences of the artificial (3rd ed.), MIT Press, Cambridge, MA, 1996
• 6: TuningFork Visualization Platform. http://tuningforkvp.sourceforge.net.

INDEX TERMS

Primary Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.3 Language Constructs and Features

General Terms:
Languages

Keywords:
programming languages curriculum