A modeling framework for reuse distance-based estimation of cache performance

We develop an analytical modeling framework for efficient prediction of cache miss ratios based on reuse distance distributions. The only input needed for our predictions is the reuse distance distribution of a program execution: previous work has shown that they can be obtained with very small overhead by sampling from native executions. This should be contrasted with previous approaches that base predictions on stack distance distributions, whose collection need significantly larger overhead or additional hardware support. The predictions are based on a uniform modeling framework which can be specialized for a variety of cache replacement policies, including Random, LRU, PLRU, and MRU (aka. bit-PLRU), and for arbitrary values of cache size and cache associativity. We evaluate our modeling framework with the SPEC CPU 2006 benchmark suite over a set of cache configurations with varying cache size, associativity and replacement policy. The introduced inaccuracies were generally below 1% for the model of the policy, and additionally around 2% when set-local reuse distances must be estimated from global reuse distance distributions. The inaccuracy introduced by sampling is significantly smaller.