Published In

MEMSYS '24: Proceedings of the International Symposium on Memory Systems

Document Type

Article

Publication Date

12-2024

Abstract

In HPC applications, memory access behavior is one of the main factors affecting performance. Improving an application’s memory access behavior requires studying spatial-temporal data locality. Ex- isting data locality analyses focus on single locations. We introduce locality metrics between pairs of memory locations that quantify three dimensions of spatial-temporal affinity: temporal access prox- imity, forward access correlation, and nearby access correlation. We describe methods for distinguishing between potential vs. realized affinity and for reasoning about affinity (or friendship) at multiple resolutions (4D, 3D, 2D, 1D). Finally, we construct spatial-temporal affinity signatures that classify memory behavior and are used to reason about changes in software (data relayout, code refactoring) or hardware (caching, prefetching). We describe methods for sig- nature visualization, interpretation, and quantitative comparison of signatures. We evaluate our methodology using applications with variants that contrast data structures, data layouts and algo- rithms. We show that spatial-temporal affinity analysis provides novel insights and enables predictive reasoning about application performance.

Rights

Copyright © 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.

DOI

10.1145/3695794.3695820

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