Inaccuracies in program profilers
Software—Practice & Experience
Quartz: a tool for tuning parallel program performance
SIGMETRICS '90 Proceedings of the 1990 ACM SIGMETRICS conference on Measurement and modeling of computer systems
The “logical clocks” approach to the visualization of parallel programs
Proceedings of the workshop on performance measurement and visualization on Performance measurement and visualization of parallel systems
Performance debugging using parallel performance predicates
PADD '93 Proceedings of the 1993 ACM/ONR workshop on Parallel and distributed debugging
Software—Practice & Experience
Finding bottlenecks in large-scale parallel programs
Finding bottlenecks in large-scale parallel programs
Gprof: A call graph execution profiler
SIGPLAN '82 Proceedings of the 1982 SIGPLAN symposium on Compiler construction
Dynamic techniques for minimizing the intrusive effect of monitoring actions
ICDCS '95 Proceedings of the 15th International Conference on Distributed Computing Systems
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Tools for performance monitoring and analysis become indispensable parts of programming environments for parallel computers. As the number of processors increases, the conventional techniques for monitoring the performance of parallel programs will produce large amounts of data in the form of event trace files. On the other hand, this wealth of information is a problem for the programmer who is forced to navigate through it, and for the tools that must store and process it. What makes this situation worse is that most of the time, a large amount of the data are irrelevant to understanding the performance of an application. In this paper, we present a new approach for collecting performance data. By tracing all the events but storing only the statistics of the performance, our approach can provide accurate and useful performance information yet require far less data to be stored. In addition, this approach also supports real-time performance monitoring.