PVM: a framework for parallel distributed computing
Concurrency: Practice and Experience
Abstract execution: a technique for efficiently tracing programs
Software—Practice & Experience
Two-dimensional signal and image processing
Two-dimensional signal and image processing
An integrated compilation and performance analysis environment for data parallel programs
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Queueing networks and Markov chains: modeling and performance evaluation with computer science applications
IPS-2: The Second Generation of a Parallel Program Measurement System
IEEE Transactions on Parallel and Distributed Systems
Distributed Performance Monitoring: Methods, Tools, and Applications
IEEE Transactions on Parallel and Distributed Systems
A Mathematical Theory of Communication
A Mathematical Theory of Communication
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While monitoring, instrumented long running parallel applications generate huge amount of instrumentation data. Processing and storing this data incurs overhead, and perturbs the execution. Techniques that eliminates unnecessary instrumentation data and lower the intrusion without loosing any performance information is valuable to tool developers. This paper presents a new algorithm for software instrumentation to measure the amount of information content of instrumentation data to be collected. The algorithm is based on entropy concept introduced in information theory, and it makes selective data collection for a time-driven software monitoring system possible.