The widening memory gap reduces performance of applications with poor data locality. Therefore, there is a need for methods to analyze data locality and help application optimization. In this paper we present StatCache, a novel sampling-based method for performing data-locality analysis on realistic workloads. StatCache is based on a probabilistic model of the cache, rather than a functional cache simulator. It uses statistics from a single run to accurately estimate miss ratios of fully-associative caches of arbitrary sizes and generate working-set graphs. We evaluate StatCache using the SPEC CPU2000 benchmarks and show that StatCache gives accurate results with a sampling rate as low as 10/sup -4/. We also provide a proof-of-concept implementation, and discuss potentially very fast implementation alternatives.