Interactive visualizations of large-scale datasets can greatly benefit from parallel rendering on a cluster with hardware accelerated graphics by assigning all rendering client nodes a fair amount of work each. However, interactivity regularly causes unpredictable distribution of workload, especially on large tiled displays. This requires a dynamic approach to adapt scheduling of rendering tasks to clients, while also considering data locality to avoid expensive I/O operations. This article discusses a dynamic parallel rendering load balancing method based on work packages which define rendering tasks. In the presented system, the nodes pull work packages from a centralized queue that employs a locality-aware dynamic affinity model for work package assignment. Our method allows for fully adaptive implicit workload distribution for both sort-first and sort-last parallel rendering.