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- W4320458520 abstract "This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs. To this end, we run microbenchmarks and realistic proxy applications with the regular compute-communicate structure on two different supercomputing platforms and choose the per-process performance and MPI time per time step as relevant observables. Using principal component analysis, clustering techniques, correlation functions, and a new phase space plot, we show how desynchronization patterns (or lack thereof) can be readily identified from a data set that is much smaller than a full MPI trace. Our methods also lead the way towards a more general classification of parallel program dynamics." @default.
- W4320458520 created "2023-02-14" @default.
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- W4320458520 date "2022-05-27" @default.
- W4320458520 modified "2023-09-27" @default.
- W4320458520 title "Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications" @default.
- W4320458520 doi "https://doi.org/10.1007/978-3-031-30442-2_12" @default.
- W4320458520 hasPublicationYear "2022" @default.
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