Mark H. Nodine

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We present an algorithm for sorting efficiently with parallel two-level memories. Our main result is an elegant, easy-to-implement, optimal, <italic>deterministic</italic> algorithm for external sorting with <italic>D</italic> disk drives. This result answers in the affirmative the open problem posed by Vitter and Shriver of whether an optimal algorithm(More)
We present an elegant deterministic load balancing strategy for distribution sort that is applicable to a wide variety of parallel diska and parallel memory hierarchies with both single and parallel processors. The simplest application of the strategy is an optimal deterministic algorithm for external sorting with multiple disks and parallel processors. In(More)
We present several algorithms for sorting efficiently with parallel two-level and multilevel memories. Our main result is an elegant, easy-to-implement, optimal, deterministic algorithm for external sorting with P disk drives. This result answers the open problem posed by Vitter and Shriver. Our measure of performance is the number of parallel in-put/output(More)
We present several eecient algorithms for sorting on the uniform memory hierarchy UMH, introduced by Alpern, Carter, and Feig, and its paral-lelization P-UMH. We give optimal and nearly-optimal algorithms for a wide range of bandwidth degradations, including a parsimonious algorithm for constant bandwidth. We also develop optimal sorting algorithms for all(More)
In this paper we introduce inputloutput (I/O) overhead . 1c, as a complexity measure for VLSI implementations of two-dimensional lattice computations of the type arising in the simulation of physical systems. We show by pebbling arguments that. 1c, = s2(n-') when there are n2 processing elements available. If the results are required to be observed at every(More)
In this paper, we address the problem of estimating cardinalities of queries over sets of objects. We base our estimates on a knowledge of subset relationships between sets (e.g., Students form a subset of People). Previous work on cardinality estimation has assumed that every subset is a representative sample of the set it is taken from. We maintain(More)