With the rapid improvement of processor speed, performance of the memory hierarchy has become the principal bottleneck for most applications. A number of compiler transformations have been developed to improve data reuse in cache and registers, thus reducing the total number of direct memory accesses in a program. Until now, however, most data reuse transformations have been static ---applied only at compile time. As a result, these transformations cannot be used to optimize irregular and dynamic applications, in which the data layout and data access patterns remain unknown until run time and may even change during the computation.In this paper, we explore ways to achieve better data reuse in irregular and dynamic applications by building on the inspector-executor method used by Saltz for run-time parallelization. In particular, we present and evaluate a dynamic approach for improving both computation and data locality in irregular programs. Our results demonstrate that run-time program transformations can substantially improve computation and data locality and, despite the complexity and cost involved, a compiler can automate such transformations, eliminating much of the associated run-time overhead.