A primary concern of future high performance systems is the way data movement is managed; the sheer scale of data to be processed directly affects the achievable performance these systems can attain. However, the increasingly complex but inherently symbiotic relationships between upcoming scientific applications and high-performance architectures necessitate increasingly informative and flexible tools to ensure performance goals are met.

In this work we develop a memory-hierarchy model that quantifies a given application’s cache behavior. What makes this work unique is that we instrument code at compile time, gather architecture-independent data at run time using a generic memory-hierarchy model, and delay selecting a particular cache hierarchy (levels, sizes, and associativities) to a post-processing step, where cache performance can be derived rapidly without having to re-run a slow cache simulator. We show that this approach is capable of predicting cache misses to within 13% of what is predicted by a traditional, high-fdelity, but slow cache simulator

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