MPI Collective Algorithm Selection in the Presence of Process Arrival Patterns

• Published in: Proceedings / IEEE International Conference on Cluster Computing pp. 108 - 119
• Main Authors: Beni, Majid Salimi, Cosenza, Biagio, Hunold, Sascha
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.09.2024
• Subjects: Algorithm Selection; Benchmark testing; Clustering algorithms; Collective Communication Operations; Libraries; Library Tuning; Message passing; Message Passing Interface (MPI); Process Arrival Patterns; Programming; Runtime; Semantics; Sensitivity; Synchronization
• ISSN: 2168-9253
• DOI: 10.1109/CLUSTER59578.2024.00017

The Message Passing Interface (MPI) is a programming model for developing high-performance applications on large-scale machines. A key component of MPI is its collective communication operations. While the MPI standard defines the semantics of these operations, it leaves the algorithmic implementation to the MPI libraries. Each MPI library contains various algorithms for each collective, and selecting the best algorithm typically relies on performance metrics obtained from micro-benchmarks. In such micro-benchmarks, processes are typically synchronized using an MPI_Barrier before invoking a collective operation. However, in real-world scenarios, processes often arrive at a collective in diverse patterns, often due to resource contention. The performance of collective algorithms can vary significantly depending on the arrival pattern type. In this work, we address the challenge of selecting the most efficient algorithm for a given collective, taking into account process arrival patterns. First, we demonstrate through a simulation study that arrival patterns significantly influence the choice of the optimal collective algorithm for specific communication instances. Second, we conduct a comprehensive micro-benchmark analysis to illustrate the sensitivity of MPI collectives to these arrival patterns. Third, we show that our innovative micro-benchmarking methodology is effective in selecting the best-performing collective algorithm for real-world applications.