An evaluation of search tree techniques in the presence of caches

Abstract

Two techniques underlie the design of commonly used search-tree algorithms: move commonly accessed items closer to the root (move-to-front) and keep the tree balanced (keep-it-balanced). The move-to-front technique tries to improve the performance for the common case by reducing the number of operations required to retrieve heavily accessed items. The keep-it-balanced technique tries to improve worst-case performance by reducing the maximum number of operations required to look up an item in the tree. In this paper, we evaluate these techniques in the presence of a cache hierarchy. As representatives of move-to-front algorithms we use splay trees. As representatives of keep-it-balanced algorithms we use B-Trees and B*-Trees. In addition to classic versions of these techniques, we evaluate variants that have been optimized for a cache hierarchy. To drive our evaluation, we use a suite of synthetic datasets. These data-sets vary primarily in the degree of locality found in the request stream and the operation mix. The major result of this paper is a qualitative performance difference analysis of the two classes of algorithms based on the expected data input characteristics. Based on this analysis, we can predict which algorithm performs faster for a large class of input data-sets. We also present guidelines for choosing implementation parameters in the presence of a cache hierarchy for all