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  Scalable Performance Analysis: The Pablo Performance Analysis Environment (1993) [127 citations — 19 self]

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by Daniel A. Reed, Ruth A. Aydt, Roger J. Noe, Phillip C. Roth, Keith A. Shields, Bradley W. Schwartz, Luis F. Tavera
In Proceedings of the Scalable Parallel Libraries Conference, A. Skjellum, Ed. IEEE Computer Society
http://www.cs.wisc.edu/~pcroth/Pablo/ScaleWork.ps.gz
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Abstract:

Developers of application codes for massively parallel computer systems face daunting performance tuning and optimization problems that must be solved if massively parallel systems are to fulfill their promise. Recording and analyzing the dynamics of application program, system software, and hardware interactions is the key to understanding and the prerequisite to performance tuning, but this instrumentation and analysis must not unduly perturb program execution. Pablo is a performance analysis environment designed to provide unobtrusive performance data capture, analysis, and presentation across a wide variety of scalable parallel systems. Current efforts include dynamic statistical clustering to reduce the volume of data that must be captured and complete performance data immersion via head-mounted displays. 1

Citations

1357 Algorithms for clustering data – Jain, Dubes - 1988
88 An overview of the pablo performance analysis environment – Reed - 1992
53 Scalable performance environments for parallel systems – Reed, Olson, et al. - 1991
19 Visualizing Parallel Computer System Performance – Malony, Reed - 1990
19 Integrating Performance Data Collection, Analysis, and Visualization – Malony, Reed, et al. - 1989
17 SDDF: The pablo self-describing data format – AYDT - 1994
7 Eds. Dynamic Graphics for Statistics – Cleveland, MiGill - 1988
7 Challenges 1993: High Performance Computing and Communications – Grand
7 A framework for sonification design – Madhyastha, Reed - 1994