Accurate estimation of configurational entropy from the in silico-generated biomolecular ensembles, e.g., from molecular dynamics (MD) trajectories, is dependent strongly on exhaustive sampling for physical reasons. This, however, creates a major computational problem for the subsequent estimation of configurational entropy using the Maximum Information Spanning Tree (MIST) or Mutual Information Expansion (MIE) approaches for internal molecular coordinates. In particular, the available software for such estimation exhibits serious limitations when it comes to molecules with hundreds or thousands of atoms, because of its reliance on a serial program architecture. To overcome this problem, we have developed a parallel, hybrid MPI/openMP C++ implementation of MIST and MIE, called PARENT, which is particularly optimized for high-performance computing and provides efficient estimation of configurational entropy in different biological processes (e.g., protein-protein interactions). In addition, PARENT also allows for a detailed mapping of intramolecular allosteric networks. Here, we benchmark the program on a set of 1-μs-long MD trajectories of 10 different protein complexes and their components, demonstrating robustness and good scalability. A direct comparison between MIST and MIE on the same dataset demonstrates a superior convergence behavior for the former approach, when it comes to total simulation length and configurational-space binning.