Abstract
Root cause diagnosis of large-scale HPC applications often fails because tools, specifically trace-based ones, can no longer record all metrics they measure. We address this problems by combining customized tracing and providing support for in-situ data analysis via ScalaJack, a framework with customizable instrumentation and pluggable extension capabilities for problem directed instrumentation and in-situ data analysis. We further eliminate cross cutting concerns by code refactoring for aspect orientation and evaluate these capabilities in case studies within and beyond the scope of tracing.
This work was supported in part by NSF grants 1217748, 0958311, and 0937908.
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Ananthakrishnan, S.K., Mueller, F. (2014). ScalaJack: Customized Scalable Tracing with In-situ Data Analysis. In: Silva, F., Dutra, I., Santos Costa, V. (eds) Euro-Par 2014 Parallel Processing. Euro-Par 2014. Lecture Notes in Computer Science, vol 8632. Springer, Cham. https://doi.org/10.1007/978-3-319-09873-9_2
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DOI: https://doi.org/10.1007/978-3-319-09873-9_2
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