Because of the speed and data rates of time-resolved experiments at facilities such as synchrotron beamlines, automation is critical during time-resolved experiments. In 3D imaging experiments like microCT (CT), this includes recognizing features of interest and 'zooming in' spatially and temporally to those features; ideally without requiring advanced information about which features are being imaged. Digital Volume Correlation (DVC) can achieve this by measuring the deformation field between images, but has not been used during autonomous experiments because of the scalability of the codes. In this work, we propose a model for global DVC and a parallel algorithm for solving it for large-scale images, suitable for giving feedback for autonomous experiments at synchrotron-based microCT beamlines. In particular, we leverage recent advancements in entropy-regularized optimal transport to develop efficient, simple-to-implement, parallel algorithms which scale linearly (O(N)) in space and time, where N is the number of voxels, and well with an increasing number of processors. As a demonstration, we compute the deformation field for every voxel from a CT volume with dimensions 2560x2560x2160. We discuss implementation details, drawbacks and future directions.