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In this paper, we present cache-efficient algorithms for trie search. There are three key features of these algorithms. First, they use different data structures (partitioned-array, B-tree, hashtable, vectors) to represent different nodes in a trie. The choice of the data structure depends on cache characteristics as well as the fanout of the node. Second, they adapt to changes in the fanout at a node by dynamically switching the data structure used to represent the node. Third, the size and the layout of individual data structures is determined based on the size of the symbols in the alphabet as well as characteristics of the cache(s). We evaluate the performance of these algorithms on real and simulated memory hierarchies. Our evaluation indicates that these algorithms outperform alternatives that are otherwise efficient but do not take cache characteristics into consideration. A comparison of the number of instructions executed indicates that these algorithms derive their performance advantage primarily by making better use of the memory hierarchy.