Continuous profiling: where have all the cycles gone?
Proceedings of the sixteenth ACM symposium on Operating systems principles
Managing performance analysis with dynamic statistical projection pursuit
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
A framework for reducing the cost of instrumented code
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Journal of Systems and Software
Sampling-based program execution monitoring
Proceedings of the ACM SIGPLAN/SIGBED 2010 conference on Languages, compilers, and tools for embedded systems
High Resolution Program Flow Visualization of Hardware Accelerated Hybrid Multi-core Applications
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Using AOP for detailed runtime monitoring instrumentation
WODA '09 Proceedings of the Seventh International Workshop on Dynamic Analysis
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Performance profiling consists of monitoring a software system during execution and then analyzing the obtained data. There are two ways to collect profiling data: event tracing through code instrumentation and statistical sampling. These two approaches have different advantages and drawbacks. This paper proposes a hybrid approach to data collection that combines the completeness of event tracing with the low cost of statistical sampling. We propose to maximize the weighted amount of information obtained during data collection, show that such maximization can be performed in linear time or is NP-hard depending on the data collected and the collection implementation. We propose an approximation algorithm for NP-hard case. Our paper also presents an application of the formal approach to an example use case.