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

arXiv: https://arxiv.org/abs/2205.13963 · HTML: https://mlsanity.com/paper/2205.13963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 3 of 3)

- [The Role of Idle Waves, Desynchronization, and Bottleneck Evasion in the Performance of Parallel Programs](https://mlsanity.com/paper/2205.04190/) (2022)
- [Desynchronization and Wave Pattern Formation in MPI-Parallel and Hybrid Memory-Bound Programs](https://mlsanity.com/paper/2002.02989/) (2020)
- [Analytic Modeling of Idle Waves in Parallel Programs: Communication, Cluster Topology, and Noise Impact](https://mlsanity.com/paper/2103.03175/) (2021)

## Abstract (arXiv)

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.