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arXiv · 2205.13963

Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications

Ayesha Afzal·Georg Hager·Gerhard Wellein·Stefano Markidis
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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.

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Ayesha Afzal, Georg Hager, Gerhard Wellein, Stefano Markidis. 2022-05-27. Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications. https://doi.org/10.1007/978-3-031-30442-2_12

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