Set-associative caches are traditionally managed using hardware-based lookup and replacement schemes that have high energy overheads. Ideally, the caching strategy should be tailored to the application's memory needs, thus enabling optimal use of this on-chip storage to maximize performance while minimizing power consumption. However, doing this in hardware alone is difficult due to hardware complexity, high power dissipation, overheads of dynamic discovery of application characteristics, and increased likelihood of making locally optimal decisions. The compiler can instead determine the caching strategy by analyzing the application code and providing hints to the hardware. We propose a hardware/software co-managed partitioned cache architecture in which enhanced load/store instructions are used to control fine-grain data placement within a set of cache partitions. In comparison to traditional partitioning techniques, load and store instructions can individually specify the set of partitions for lookup and replacement. This fine grain control can avoid conflicts, thus providing the performance benefits of highly associative caches, while saving energy by eliminating redundant tag and data array accesses. Using four direct-mapped partitions, we eliminated 25% of the tag checks and recorded an average 15% reduction in the energy-delay product compared to a hardware-managed 4-way set-associative cache.