On modern computer systems, the memory performance of an application depends on its locality. For a single execution, locality-correlated measures like average miss rate or working-set size have long been analyzed using reuse distance —the number of distinct locations accessed between consecutive accesses to a given location. This article addresses the analysis problem at the program level, where the size of data and the locality of execution may change significantly depending on the input. The article presents two techniques that predict how the locality of a program changes with its input. The first is approximate reuse-distance measurement, which is asymptotically faster than exact methods while providing a guaranteed precision. The second is statistical prediction of locality in all executions of a program based on the analysis of a few executions. The prediction process has three steps: dividing data accesses into groups, finding the access patterns in each group, and building parameterized models. The resulting prediction may be used on-line with the help of distance-based sampling. When evaluated on fifteen benchmark applications, the new techniques predicted program locality with good accuracy, even for test executions that are orders of magnitude larger than the training executions. The two techniques are among the first to enable quantitative analysis of whole-program locality in general sequential code. These findings form the basis for a unified understanding of program locality and its many facets. Concluding sections of the article present a taxonomy of related literature along five dimensions of locality and discuss the role of reuse distance in performance modeling, program optimization, cache and virtual memory management, and network traffic analysis.