Umbrella sampling is one of the most successful computational methods for enhancing the sampling of rare events in molecular systems. Nonequilibrium Umbrella Sampling (NEUS) is a version of umbrella sampling that is suitable for use in systems driven far from equilibrium. In the first part of the talk, I will present a novel formulation of the NEUS method and highlight how this framework can be readily extended to recover dynamical information in the form of long-time correlation functions. In the second part of the talk, I will present a detailed overview of the development of software tools we call the Enhanced Sampling Toolkit that enable rapid prototyping of enhanced sampling algorithms for HPC applications. The Enhanced Sampling Toolkit is designed to allow for rapid development of algorithms that are written in Python and parallelized in MPI while executing computationally expensive molecular dynamics within a higher-performance compiled code such as LAMMPS.