MemFriend: Understanding Memory Performance with Spatial-Temporal Affinity
Yasodha Suriyakumar, Nathan R. Tallent, Andrés Márquez, Karen L. Karavanic, Ozgur O. Kilic · Proceedings of the International Symposium on Memory Systems · 2024
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.