Department of Information Technology
Uppsala Architecture Research Team

StatCache/StatStack Statistical Cache Modeling

Statistical cache modeling is a collection of techniques for rapidly modeling the miss ratio of a cache or hierarchy of caches from low-overhead sparse data collected at runtime. StatCache and StatStack are novel sampling-based methods for performing data-locality analysis on realistic workloads. They are based on probabilistic models of the cache, rather than a functional cache simulator. The models use statistics from a single application run to accurately estimate miss ratios of fully-associative random and LRU caches of arbitrary sizes and generate working-set (miss ratio as a function of cache size) graphs. StatCache and StatCC have been evaluated using the SPEC benchmarks and shown to gives accurate results with a sampling rate as low as 10^(-4). This technology was commercialized as part of the ThreadSpotter(TM) tools from RogueWave.

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Statistical Cache Modeling first samples architecturally independent reuse distances and then uses those to model behavior on arbitrary cache hierarchies.

Updated  2013-07-05 14:21:17 by David Black-Schaffer.