Verifying Performance Guidelines for MPI Collectives at Scale

Abstract

MPI collective communication operations are crucial for high-performance computing, making the efficient implementation of collective algorithms essential for optimal application performance. While most MPI libraries provide several algorithms for a specific collective operation, each may work better in a specific scenario. Therefore, selecting the most suitable algorithm for each use case is important. However, even the best algorithm in a given MPI library’s set may deliver suboptimal performance. Self-consistent MPI performance guidelines are general expectations that collectives must meet to be deemed performance-consistent. Specifically, a specialized collective call should not be slower than its less specialized counterparts. This paper introduces a tool for assessing the performance consistency of MPI collectives in a statistically sound manner. Through a case study, we demonstrate the current state of MPI performance consistency for three TOP500 machines.