Quantifying and Modeling Irregular MPI Communication
Carson Woods, Derek Schafer, Patrick G. Bridges, Anthony Skjellum · 2024
Many modern scientific applications have communication patterns where both the number of communication partners and amount of data transmitted between process pairs vary significantly and change over time. This work describes an approach to measure and model the behavior of these irregular, dynamic MPI communication patterns on modern high-performance computing systems. Specifically, this approach quantifies communication behavior using a small number of stochastic random variables that capture key features of irregular communication patterns, and estimates the distributions of these variables either parameterically or empirically. This work then demonstrates that the collected parameters and their distributions can be used to measure and model the communication performance of several MPI applications. It also presents a synthetic benchmark that uses these distributions to recreate statistically similar communication patterns. This approach provides a lightweight method to reproduce communication patterns of a variety of applications with minimal overhead while gaining additional insights into the performance and characteristics of various irregular communication patterns.