Prioritiles

Prioritiles is a framework for tiling a domain into discrete units called prioritiles, each with an associated priority score that reflects its importance for a given objective. The approach is used to focus computational or rendering effort on regions most likely to affect outcomes, while leaving lower-priority areas for later processing.

In a typical implementation, a domain is partitioned into tiles; each tile stores its bounds, state, and a priority score. A priority queue orders tiles by score. Processing proceeds by repeatedly removing the highest-priority tile and updating neighboring tiles' priorities as new information arrives or as the model evolves.

Applications span geographic information systems, real-time rendering, adaptive meshing in simulations, data caching and streaming, and sensor networks where data density or error estimates vary spatially.

Priority calculation may use information gain, estimated error, data density, or user-defined importance. Common scheduling strategies include dynamic refinement, coarsening, and staged rendering. Maintaining priorities requires incremental updates when data changes, as well as bounds to prevent excessive refinement.

Prioritiles enable targeted computation, reduced latency, and scalable resource use. They depend on a suitable scoring function and robust update rules; miscalibration can bias results or create lag. The approach adds overhead for maintaining the priority structure and coherence among tiles.

The term prioritiles emerged in tiling and adaptive discretization literature during the 2010s as a means to describe prioritized tiling approaches. It is related to, but distinct from, adaptive mesh refinement and level-of-detail methods.

See also adaptive mesh refinement, level of detail, priority queues, tiling, information gain.