The rapid increase in power consumption of high performance computing (HPC) systems has led to an increase in the amount of cooling resources required to operate these facilities at a reliable threshold. The cooling systems contribute a large portion of the total power consumption of the facility, thus driving up the costs of providing power to these facilities. In addition, when cores sharing resources (e.g., last-level cache) execute applications at the same time, they can experience contention and therefore performance degradation. By taking a holistic approach to HPC facility management through intelligently allocating both computing and cooling resources, the performance of the HPC system can be maximized by considering co-location while obeying power consumption and thermal constraints. The performance of the system is quantified as the total reward earned from completing tasks by their individual deadlines. We propose three novel resource allocation techniques to maximize performance under power and thermal constraints when considering co-location effects: (1) a greedy heuristic, (2) a genetic algorithm technique used in combination with a new local search technique that guarantees the power and thermal constraints, and (3) a nonlinear programming based approach (from previous work), adapted to consider co-location effects.