The information hiding philosophy of object-oriented programming encourages localizing data structures within objects rather than sharing data globally across different classes of objects. This emphasis on local data leads naturally to fine-grained data abstractions, particularly in scientific simulations involving large collections of small, discrete physical or mathematical objects. This paper focuses on a subset of such simulations where dynamically reconfigurable links bind the objects together. It is demonstrated that fine-grained data structures reduce the complexity of local operations on the data at the potential expense of increased global operation complexity. Two metrics are used to describe data structures: granularity is the number of instantiations required to cover the data space, whereas extent is the continuously traversable length of the data along a given direction. These definitions are applied to two abstractions for simulating the turbulent motion of quantum vortices in superfluid liquid helium. Several local and global operations on a fine-grained linked list are compared with those on a coarse-grained array. It is demonstrated that fine-grained data structures recover the simplicity of more coarse-grained structures if maximal extent is maintained as the granularity increases.