4 edition of **Tight comparison bounds on the complexity of parallel sorting** found in the catalog.

- 131 Want to read
- 20 Currently reading

Published
**1986**
by Courant Institute of Mathematical Sciences, New York University in New York
.

Written in English

**Edition Notes**

Statement | by Yassi Azar, Uzi Vishkin. |

Series | Ultracomputer note -- 103 |

Contributions | Vishkin, U. |

The Physical Object | |
---|---|

Pagination | 11 p. |

Number of Pages | 11 |

ID Numbers | |

Open Library | OL17979107M |

running time complexity depends on the sorting process. C. Closest Pair Problem In this problem we need to find pair of elements that have smallest difference. Input elements are arrange in sorted sequence than closest pair will present next to each other. D. Frequency Distribution quick sort and parallel quick sort algorithms. We know that this must be the complexity of the problem meaning the lower bound is tight because Horner's rule evaluates the polynomial in Θ(n). An example of a trivial lower bound based on the output of the problem is an algorithm generating all the permutations of an array must cost at least Ω(n!).

Sorting and Algorithm Analysis Computer Science E Harvard Extension School Fall David G. Sullivan, Ph.D. • The time efficiencyor time complexity of an algorithm is some Big-O Notation and Tight Bounds • Big-O notation provides an upper bound, not a tight bound (upper and lower). parallel sorting algorithms are parallel versions of radix sort and quicksort [4, 17], column sort [10], and Cole’s parallel merge sort [6]. Given the large number of parallel sorting algorithms and the wide variety of parallel architectures, it is a difﬁcult task to select the best algorithm for a particular machine and problem instance.

Because we expect the number parallel comparison rounds and the total number of comparisons to be the main performance bottlenecks, we are inter-ested here in studying the equivalence class sorting problem in Valiant’s parallel comparison model [21], which . Parallel Sorting: The fundamental operation of comparison- based sorting is compare-exchange. The lower bound on any comparison-based sort of n numbers is Θ(nlog n). The sorted list is partitioned with the property that each partitioned list is sorted and each element in processor Pi's list is less than that in Pj's list if i.

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() The average complexity of deterministic and randomized parallel comparison sorting algorithms. 28th Annual Symposium on Foundations of Computer Science (sfcs ), () Tight complexity bounds for parallel comparison by: Tight complexity bounds for parallel comparison sorting Conference Paper (PDF Available) in Foundations of Computer Science,16th Annual Symposium on November with 13 Reads.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The problem of sorting n elements using p processors in a parallel comparison model is considered. Lower and upper bounds which imply that for p ³ n, the time complexity of this problem is Q(log(1 + p / n) logn _____) are presented.

This complements [AKS] in settling the problem since the AKS sorting network. BibTeX @INPROCEEDINGS{Alon86tightcomplexity, author = {Noga Alon and Yossi Azar}, title = {Tight Complexity Bounds for Parallel Comparison Sorting}, booktitle = {Proc.

of 29th IEEE Symp. on Foundations of Computer Science}, year = {}, pages = {}}. Tight Comparison Bounds On The Complexity Of Parallel Sorting. By Yossi Azar and Uzi Vishkin. Abstract.

The problem of sorting n elements using p processors in a parallel comparison model is considered. Lower and upper bounds which imply that for p ³ n, the time complexity of this problem is Q(log(1 + p / n) logn _____) are presented. Cited by: This implies that for every fixed k, any deterministic comparison sort algorithm must be asymptotically worse than this randomized algorithm.

The lower bound improves on Häggkvist-Hell's lower bound. We show that "approximate sorting" in time 1 requires asymptotically more than nlogn processors.

This settles a problem raised by M. Rabin. After combining the above two facts, we get following relation. comparison based sorting algorithm must make at least nLog 2 n comparisons to sort the input array, and Heapsort and merge sort are asymptotically optimal comparison sorts. comparison sort in time k with an expected number of O(nl+l/k) comparisons.

This implies that for every fixed k, any deterministic comparison sort algorithm must be asymptotically worse than this randomized algorithm. The lower bound improves on Haggkvist Hell's lower bound. We show that "approximate sorting" in time 1.

This completes tlxe proof. r References [1] N. Alon and Y. Azar, The average complexity of deterministic and randomized parallel comparison-sorting algorithms, SIAM J. Comput. 17 () [21 Y. Azar and U. vishkin, Tight comparison bounds on the complexity of parallel sorting, SIAM J.

Cortrput. l() Reviewer: Ian Parberry Early research into sorting focused on in situ comparison-based sorting algorithms. Such an algorithm is said to be oblivious (a term which can be traced, in a different context, to Paterson) if the sequence of cells accessed is dependent on the number of cells but not on their contents.

In this paper, we prove tight upper and lower bounds on the number of processors, information transfer, wire area, and time needed to sort N numbers in a bounded-degree fixed-connection network.

Asymptotic upper bound means that a given algorithm executes during the maximum amount of time, depending on the number of inputs. Let's take a sorting algorithm as an example. If all the elements of an array are in descending order, then to sort them, it will take a running time of O(n), showing upper bound the array is already sorted, the value will be O(1).

Using the parallel comparison tree model of Valiant, we study the time required in the worst case to select the median of n elements with p processors.

With a miniscule improvement of recent work by Ajtai, Komlós, Steiger and Szemerédi, we show that it is Θ n p+ log log n log (1+ p n) for 1≤p≤(n 2).This expression is equivalent to one established by Kruskal as the time required to merge. Optimal parallel string algorithms: sorting, merging and computing the minimum.

We use the result to show lower bounds on the I/O-complexity on a number of problems where known techniques only give trivial bounds. Among these are the problems of removing duplicates from a multiset, a problem of great importance in e.g.

relational data-base systems, and the problem of determining the mode — the most frequently occurring. Classification. Sorting algorithms are often classified by: Computational complexity (worst, average and best behavior) in terms of the size of the list (n).For typical serial sorting algorithms good behavior is O(n log n), with parallel sort in O(log 2 n), and bad behavior is O(n 2).

(See Big O notation.)Ideal behavior for a serial sort is O(n), but this is not possible in the average case. THE COMPARISON MODEL 32 Almost-tight upper-bounds for comparison-based sorting Assume n is a power of 2 — in fact, let’s assume this for the entire rest of today’s lecture.

Can you think of an algorithm that makes at most nlgn comparisons, and so is tight in the leading term. In fact, there are several algorithms, including.

sort = O(nlogn) and optimal cache complexity Q sort(n). But the SPMS algorithm is not data-oblivious. No non-trivial data oblivious parallel sorting algorithm with optimal cache complexity was known prior to the results we present in this paper. More background on caching and multithreaded computations is given in SectionA.

Quicksort (sometimes called partition-exchange sort) is an efficient sorting ped by British computer scientist Tony Hoare in and published init is still a commonly used algorithm for sorting. When implemented well, it can be about two or three times faster than its main competitors, merge sort and heapsort.

[contradictory]. Abstract. Selim Akl has been a ground breaking pioneer in the field of parallel sorting algorithms. His ‘Parallel Sorting Algorithms’ book [], published inhas been a standard text for researchers and we discuss recent advances in parallel sorting methods for many-core GPUs.

Tight bound (big Theta) takes both upper and lower bounds into account. Technically the lower bound is Ω(1), because you can find the searched-for element on the first comparison.

See Is binary search theta log (n) or big O log(n) for further discussion of the time complexity of binary search.For example, the run time of sequential (comparison based) sorting is (nlogn).

A good parallel sorting algorithm might have run time t = nlogn p + logn. This will be asymptotically cost optimal if p = O(n).

Another parallel sorting algorithm might have run time t = nlogn p + log 2 n. This will be asymptotically cost optimal if p = O n logn.Let’s consider parallel versions.

Now suppose we wish to redesign merge sort to run on a parallel computing platform. Just as it it useful for us to abstract away the details of a particular programming language and use pseudocode to describe an algorithm, it is going to simplify our design of a parallel merge sort algorithm to first consider its implementation on an abstract PRAM machine.