May 23, 2025
Conference Paper

MemFriend: Understanding Memory Performance with Spatial-Temporal Affinity

Abstract

In HPC applications, memory access behavior is one of the main factors affecting performance. Improving an application’s memory access behavior involves optimizing data layout and/or restructuring code, and requires studying spatial-temporal data locality. Existing data locality analyses focus on single-location metrics and are restricted to evaluating temporal locality. We introduce spatial-temporal affinity metrics that quantify temporal access proximity, forward access correlation, and nearby access correlation between pairs of memory locations. We describe methods for distinguishing between potential vs. realized affinity and for reasoning about affinity at multiple resolutions (3D, 2D, 1D). Finally, we construct spatial-temporal affinity signatures that classify memory behavior and that be used to reason about changes in software (data relayout, code refactoring) or hardware (caching, prefetching). We describe methods for signature visualization, interpretation, and quantitative comparison of signatures. We evaluate our methodology using applications with variants that contrast data structures, data layouts and algorithms. We show that spatial-temporal affinity analysis provides novel insights and enables predictive reasoning about application performance when contrasted with reuse distance analysis.

Published: May 23, 2025

Citation

Suriyakumar Y., N.R. Tallent, A. Marquez, K.L. Karavanic, and O.O. Kilic. 2024. MemFriend: Understanding Memory Performance with Spatial-Temporal Affinity. In Proceedings of the International Symposium on Memory Systems (MEMSYS 2024), September 30-October 3, 2024, Washington, D.C., 270 - 284. New York, New York:Association for Computing Machinery. PNNL-SA-202822. doi:10.1145/3695794.3695820

Research topics