Recent years see cache partitioning techniques employ commercial way partitioning to great effect. Techniques like partitioning build analysis on miss curves. However, the various forms of partitioning run into the issue of core scaling, requiring multi-tenant partitions and analysis of how workloads behave when sharing ever shrinking resources. We observe surprising variations in performance which can be disjoint with variations in cache misses that encourage such analysis. We present Capacity Curve Annotation and Feature Extraction, methodologies for annotation, feature extraction, and analysis of sensitivity studies based on performance curves.
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