Classical compiler optimizations assume a fixed cache architecture and modify the program to take best advantage of it. In some cases, this may not be the best strategy because each loop nest might work best with a different cache configuration and transforming a nest for a given fixed cache configuration may not be possible due to data dependences. Working with a fixed cache configuration can also increase energy consumption in loops where the best required configuration is smaller than the default (fixed) one. In this paper, we take an alternate approach and modify the cache configuration for each nest depending on the access pattern exhibited by the nest. We call this technique compiler-directed cache polymorphism (CDCP). More specifically, in this paper, we make the following contributions. First, we present an approach for analyzing data reuse properties of loop nests. Second, we give algorithms to simulate the footprints of array references in their reuse space. Third, based on our reuse analysis, we present an optimization algorithm to compute the cache configurations for each nest. Our experimental results show that CDCP is very effective in finding the near-optimal data cache configurations for different nests in array-intensive applications.