Autonomous underwater vehicles (AUVs) have become indispensable tools for marine scientists to study the world's oceans. Real time examination of mission data can substantially enhance the overall effectiveness of AUVs in oceanography. However, current AUV technology only allows a detailed analysis of data after completion of a mission. The ability to perform on-board analysis of real time data is computationally intensive, requiring an energy efficient programming infrastructure that can be adapted to battery operated, energy constrained vehicles. Intel's 48-core SCC system exposes a collection of performance and energy/power knobs that can be refined for dynamically changing computation vs. energy tradeoffs. In this paper, we illustrate the potential benefits of these knobs for environment modeling and path planning. These applications are important for any autonomous cyber-physical system. Our experimental case study targets AUVs, particularly the Slocum glider. The results show that selecting different core, network, and memory controller speeds have a significant impact on the overall performance and energy requirements of our applications. Furthermore, the best selection is non-trivial and will depend on the available energy and computational needs of other mission critical tasks executing concurrently with modeling/path planning applications.