Premium
When to use splay trees
Author(s) -
Lee Eric K.,
Martel Charles U.
Publication year - 2007
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.813
Subject(s) - computer science , cache , overhead (engineering) , ternary search tree , tree (set theory) , heuristic , contrast (vision) , binary tree , arithmetic , parallel computing , mathematics , algorithm , interval tree , tree structure , operating system , combinatorics , artificial intelligence
In this paper we present new empirical results for splay trees. These results provide a better understanding of how cache performance affects query execution time. Our results show that splay trees can have faster lookup times compared with randomly built binary search trees (BST) under certain settings. In contrast, previous experiments have shown that because of the instruction overhead involved in splaying, splay trees are less efficient in answering queries than randomly built BSTs—even when the data sets are heavily skewed (a favorable setting for splay trees). We show that at large tree sizes the difference in cache performance between the two types of trees is significant. This difference means that splay trees are faster than BSTs for this setting—despite still having a higher instruction count. Based on these results we offer guidelines in terms of tree size, access pattern, and cache size as to when splay trees will likely be more efficient. We also present a new splaying heuristic aimed at reducing instruction count and show that it can improve on standard splaying by 10–27%. Copyright © 2007 John Wiley & Sons, Ltd.