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Sorting in Linear Time?
, 1995
"... We show that a unit-cost RAM with a word length of w bits can sort n integers in the range 0 : : 2 w \Gamma1 in O(n log log n) time, for arbitrary w log n, a significant improvement over the bound of O(n p log n) achieved by the fusion trees of Fredman and Willard. Provided that w (log n) 2+f ..."
Abstract
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Cited by 73 (15 self)
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We show that a unit-cost RAM with a word length of w bits can sort n integers in the range 0 : : 2 w \Gamma1 in O(n log log n) time, for arbitrary w log n, a significant improvement over the bound of O(n p log n) achieved by the fusion trees of Fredman and Willard. Provided that w (log n) 2+ffl for some fixed ffl ? 0, the sorting can even be accomplished in linear expected time with a randomized algorithm. Both of our algorithms parallelize without loss on a unit-cost PRAM with a word length of w bits. The first one yields an algorithm that uses O(logn) time and O(n log log n) operations on a deterministic CRCW PRAM. The second one yields an algorithm that uses O(log n) expected time and O(n) expected operations on a randomized EREW PRAM, provided that w (log n) 2+ffl for some fixed ffl ? 0. Our deterministic and randomized sequential and parallel algorithms generalize to the lexicographic sorting problem of sorting multiple-precision integers represented in several words. ...
On Sorting Strings in External Memory
, 1997
"... ) Lars Arge Paolo Ferragina y Roberto Grossi z Jeffrey Scott Vitter x Abstract. In this paper we address for the first time the I/O complexity of the problem of sorting strings in external memory, which is a fundamental component of many large-scale text applications. In the standard unit-cost RAM c ..."
Abstract
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Cited by 23 (12 self)
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) Lars Arge Paolo Ferragina y Roberto Grossi z Jeffrey Scott Vitter x Abstract. In this paper we address for the first time the I/O complexity of the problem of sorting strings in external memory, which is a fundamental component of many large-scale text applications. In the standard unit-cost RAM comparison model, the complexity of sorting K strings of total length N is \Theta(K log 2 K+N). By analogy, in the external memory (or I/O) model, where the internal memory has size M and the block transfer size is B, it would be natural to guess that the I/O complexity of sorting strings is \Theta( K B log M=B K B + N B ), but the known algorithms do not come even close to achieving this bound. Our results show, somewhat counterintuitively, that the I/O complexity of string sorting depends upon the length of the strings relative to the block size. We first consider a simple comparison I/O model, where one is not allowed to break the strings into their characters, and we sho...
On sorting strings in external memory (extended abstract
- In STOC ’97: Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
, 1997
"... Abstract. In this paper we address for the first time the I/O complexity of the problem of sorting strings in external memory, which is a fundamental component of many large-scale text applications. In the standard unit-cost RAM comparison model, the complexity of sorting K strings of total length N ..."
Abstract
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Cited by 3 (0 self)
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Abstract. In this paper we address for the first time the I/O complexity of the problem of sorting strings in external memory, which is a fundamental component of many large-scale text applications. In the standard unit-cost RAM comparison model, the complexity of sorting K strings of total length N is (K log2 K +N). By analogy, in the external memory (or I/O) model, where the internal memory has size M and the block transfer size is B, it would be natural to guess that the I/O complexity of sorting strings is ( K B logM=B K N
unknown title
"... Abstract We show that a unit-cost RAM with a word length of w bitscan sort n integers in the range 0: : 2w \Gamma 1 in O(n log log n)time, for arbitrary w * log n, a significant improvementover the bound of O(nplog n) achieved by the fusion treesof Fredman and Willard. Provided that w * (log n)2+ffl ..."
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Abstract We show that a unit-cost RAM with a word length of w bitscan sort n integers in the range 0: : 2w \Gamma 1 in O(n log log n)time, for arbitrary w * log n, a significant improvementover the bound of O(nplog n) achieved by the fusion treesof Fredman and Willard. Provided that w * (log n)2+ffl, forsome fixed ffl? 0, the sorting can even be accomplished inlinear expected time with a randomized algorithm. Both of our algorithms parallelize without loss on a unit-cost PRAM with a word length of w bits. The first one yieldsan algorithm that uses O(log n) time and O(n log log n) op-erations on a deterministic CRCW PRAM. The second one yields an algorithm that uses O(log n) expected time and O(n) expected operations on a randomized EREW PRAM,provided that w * (log n)2+ffl for some fixed ffl? 0.Our deterministic and randomized sequential and parallel algorithms generalize to the lexicographic sorting problemof sorting multiple-precision integers represented in several words. 1 Introduction Sorting is one of the most fundamental computational prob-lems, and n keys can be sorted in O(n log n) time by anyof a number of well-known sorting algorithms. These algorithms operate in the comparison-based setting, i.e., they ob-tain information about the relative order of keys exclusively through pairwise comparisons. It is easy to show that a run-ning time of
Arne Andersson
"... We show that a unit-cost RAM with a word length of w bits can sort n integers in the range 0 : : 2 w \Gamma 1 in O(n log log n) time, for arbitrary w log n, a significant improvement over the bound of O(n p log n) achieved by the fusion trees of Fredman and Willard. Provided that w (log n) 2+ ..."
Abstract
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We show that a unit-cost RAM with a word length of w bits can sort n integers in the range 0 : : 2 w \Gamma 1 in O(n log log n) time, for arbitrary w log n, a significant improvement over the bound of O(n p log n) achieved by the fusion trees of Fredman and Willard. Provided that w (log n) 2+ffl for some fixed ffl ? 0, the sorting can even be accomplished in linear expected time with a randomized algorithm. Both of our algorithms parallelize without loss on a unit-cost PRAM with a word length of w bits. The first one yields an algorithm that uses O(log n) time and O(n log log n) operations on a deterministic CRCW PRAM. The second one yields an algorithm that uses O(log n) expected time and O(n) expected operations on a randomized EREW PRAM, provided that w (log n) 2+ffl for some fixed ffl ? 0. Our deterministic and randomized sequential and parallel algorithms generalize to the lexicographic sorting problem of sorting multiple-precision integers represented in several words...

