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Proceedings ArticleDOI

An 0(n log n) sorting network

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TLDR
A sorting network of size 0(n log n) and depth 0(log n) is described, and a derived procedure (&egr;-nearsort) are described below, and the sorting network will be centered around these elementary steps.
Abstract
The purpose of this paper is to describe a sorting network of size 0(n log n) and depth 0(log n). A natural way of sorting is through consecutive halvings: determine the upper and lower halves of the set, proceed similarly within the halves, and so on. Unfortunately, while one can halve a set using only 0(n) comparisons, this cannot be done in less than log n (parallel) time, and it is known that a halving network needs (½)n log n comparisons. It is possible, however, to construct a network of 0(n) comparisons which halves in constant time with high accuracy. This procedure (e-halving) and a derived procedure (e-nearsort) are described below, and our sorting network will be centered around these elementary steps.

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References
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Proceedings ArticleDOI

Sorting networks and their applications

TL;DR: To achieve high throughput rates today's computers perform several operations simultaneously; not only are I/O operations performed concurrently with computing, but also, in multiprocessors, several computing operations are done concurrently.
Journal ArticleDOI

Space bounds for a game on graphs

TL;DR: It is shown that for each graph withn vertices and maximum in-degreed, there is a pebbling strategy which requires at mostc(d) n/logn pebbles, and this bound is tight to within a constant factor.
Proceedings ArticleDOI

Explicit constructions of linear size superconcentrators

Ofer Gabber, +1 more
TL;DR: An explicit construction of an infinite family of N-superconcentrators of density 44 of the most economical previously known explicit graphs of this type is presented.
Proceedings ArticleDOI

On non-linear lower bounds in computational complexity

TL;DR: It is shown that the graph of any algorithm for any one of a number of arithmetic problems (e.g. polynomial multiplication, discrete Fourier transforms, matrix multiplication) must have properties closely related to concentration networks.