Abstract
In event-based performance analysis the amount of collected data is one of the most urgent challenges. It can massively slow down application execution, overwhelm the underlying file system and introduce significant measurement bias due to intermediate memory buffer flushes. To address these issues we propose an in-memory event tracing approach that dynamically adapts the volume of application events to an amount that is guaranteed to fit into a single memory buffer, and therefore, avoiding file interaction entirely. These concepts include runtime filtering, enhanced encoding techniques, and novel strategies for runtime event reduction. The concepts further include the hierarchical memory buffer a multi-dimensional, hierarchical data structure allowing to realize these concepts with minimal overhead. We demonstrate the capabilities of our concepts with a prototype implementation called OTFX, based on the Open Trace Format 2, a state-of-the-art open source tracing library used by the performance analyzers Vampir, Scalasca, and Tau.
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Wagner, M., Knüpfer, A., Nagel, W.E. (2016). OTFX: An In-memory Event Tracing Extension to the Open Trace Format 2. In: Carretero, J., et al. Algorithms and Architectures for Parallel Processing. ICA3PP 2016. Lecture Notes in Computer Science(), vol 10049. Springer, Cham. https://doi.org/10.1007/978-3-319-49956-7_1
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