d-Dimensional range search on multicomputers
The range tree is a fundamental data structure for multidimensional point sets, and, as such, is central in a wide range of geometric and database applications. In this paper we describe the first nontrivial adaptation of range trees to the parallel distributed memory setting (BSP-like models). Given a set of n points in d-dimensional Cartesian space, we show how to construct on a coarse-grained multicomputer a distributed range tree T in time O(s/p + T-c(s, p)), where s = n log(d-1) n is the size of the sequential data structure and T-c(s, p) is the time to perforln an h-relation with h = Theta(s/p). We then show how T can be used to answer a given set Q of m = O (n) range queries in time O((s log m)/p+T-c (s, p)) and O ((s log m)/p+T-c (s, p)+k/p). where k is the number of results to be reported. These parallel construction and search algorithms are both highly efficient, in that their running times are the sequential time divided by the number of processors, plus a constant number of parallel communication rounds.
range search multicomputers bsp cgm parallel algorithms parallel computing data-bases