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

