Multicore processors have become an integral part of modern large-scale and high-performance parallel and distributed computing systems. Unfortunately, applications co-located on multicore processors can suffer from decreased performance and increased dynamic energy use as a result of interference in shared resources, such as memory. As this interference is difficult to characterize, assumptions about application execution time and energy usage can be misleading in the presence of co-location. Consequently, it is important to accurately characterize the performance and energy usage of applications that execute in a co-located manner on these architectures. This work investigates some of the disadvantages of co-location, and presents a methodology for building models capable of utilizing varying amounts of information about a target application and its co-located applications to make predictions about the target application’s execution time and the system’s energy use under arbitrary co-locations of a wide range of application types. The proposed methodology is validated on three different server class Intel Xeon multicore processors using eleven applications from two scientific benchmark suites. The model’s utility for scheduling is also demonstrated in a simulated large-scale high-performance computing environment through the creation of a co-location aware scheduling heuristic. This heuristic demonstrates that scheduling using information generated with the proposed modeling methodology is capable of making significant improvements over a scheduling heuristic that is oblivious to co-location interference.