Crafting Data Structures: A Study of Reference Locality in Refinement-Based Pathfinding
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The widening gap between processor speed and memory latency increases the importance of crafting data structures and algorithms to exploit temporal and spatial locality. Refinement-based pathfinding algorithms, such as Classic Refinement, find near-optimal paths in very large sparse graphs where traditional search techniques fail to generate paths in acceptable time. In this paper we present a performance evaluation study of three simple data structure transformation oriented techniques aimed at improving the data reference locality of Classic Refinement. In our experiments these techniques improved data reference locality resulting in consistently positive performance improvements upwards of 51.2%. In addition, these techniques appear to be orthogonal to compiler optimizations and robust with respect to hardware architecture.
KeywordsImage Mapping Reference Locality Abstraction Level Input Graph Freight Transport
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