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.
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Efficient cache modeling with sparse data. In Processor and System-on-Chip Simulation, pp 193-209, Springer, New York, 2010. (DOI).
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StatStack: Efficient modeling of LRU caches. In Proc. International Symposium on Performance Analysis of Systems and Software: ISPASS 2010, pp 55-65, IEEE, Piscataway, NJ, 2010. (DOI).
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StatCache: A Probabilistic Approach to Efficient and Accurate Data Locality Analysis. In 2004 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS-2004),, 2004.
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StatCache: A Probabilistic Approach to Efficient and Accurate Data Locality Analysis. In Proceedings of the 2004 IEEE International Symposium on Performance Analysis of Systems and Software, 2004.