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While the cost of computation is an easy to understand local property, thecost of data movement on cached architectures depends on global state, does notcompose, and is hard to predict. As a result, programmers often fail toconsider the cost of data movement. Existing cache models and simulatorsprovide the missing information but are computationally expensive. We present alightweight cache model for fully associative caches with least recently used(LRU) replacement policy that gives fast and accurate results. We count thecache misses without explicit enumeration of all memory accesses by usingsymbolic counting techniques twice: 1) to derive the stack distance for eachmemory access and 2) to count the memory accesses with stack distance largerthan the cache size. While this technique seems infeasible in theory, due tonon-linearities after the first round of counting, we show that the countingproblems are sufficiently linear in practice. Our cache model often computesthe results within seconds and contrary to simulation the execution time ismostly problem size independent. Our evaluation measures modeling errors below0.6% on real hardware. By providing accurate data placement information weenable memory hierarchy aware software development.

A Fast Analytical Model of Fully Associative Caches