A simple and fast parallel coloring for distance-hereditary graphs

In the literature, there are quite a few sequential and parallel algorithms to solve problems on distance-hereditary graphs. Two well-known classes of graphs, which contain trees and cographs, belong to distance-hereditary graphs. We consider the vertex-coloring problem on distance-hereditary graphs. Let T/sub d/(|V|, |E|) and P/sub d/d(|V|, |E|) denote the time and processor complexities, respectively, required to construct a decomposition tree representation of a distance-hereditary graph G=(V,E) on a PRAM model M/sub d/. Our algorithm runs in O(T/sub d/(|V|, |E|)+log|V|) time using O(P/sub d/(|V|, |E|)+|V|/log|V|) processors on M/sub d/. The best known result for constructing a decomposition tree needs O(log/sup 2/ |V|) time using O(|V|+|E|) processors on a CREW PRAM. If a decomposition tree is provided as input, we solve the problem in O(log |V|) time using O(|V|/log |V|) processors on an EREW PRAM. To the best of our knowledge, there is no parallel algorithm for this problem on distance-hereditary graphs.     

An efficient parallel recognition algorithm forbipartite-permutation graphs

We present a parallel recognition algorithm for bipartite-permutation graphs. The algorithm can be executed in O(log n) time on the CRCW PRAM if O(n³/log n) processors are used, or O(log² n) time on the CREW PRAM if O(n³/log² n) processors are used. Chen and Yesha (1993) have presented another CRCW PRAM algorithm that takes O(log²n) time if O(n ³) processors are used. Compared with Chen and Yesha's algorithm, our algorithm requires either less time and fewer processors on the same machine model, or fewer processors on a weaker machine model. Our algorithm can also be applied to determine if two bipartite-permutation graphs are isomorphic     

Fully dynamic maintenance of k-connectivity in parallel

Given a graph G=(V, E) with n vertices and m edges, the k-connectivity of G denotes either the k-edge connectivity or the k-vertex connectivity of G. In this paper, we deal with the fully dynamic maintenance of k-connectivity of G in the parallel setting for k=2, 3. We study the problem of maintaining k-edge/vertex connected components of a graph undergoing repeatedly dynamic updates, such as edge insertions and deletions, and answering the query of whether two vertices are included in the same k-edge/vertex connected component. Our major results are the following: (1) An NC algorithm for the 2-edge connectivity problem is proposed, which runs in O(log n log(m/n)) time using O(n^3/4) processors per update and query. (2) It is shown that the biconnectivity problem can be solved in O(log^{2 n} ) time using O(nα(2n, n)/logn) processors per update and O(1) time with a single processor per query or in O(log n log_n/^m) time using O(nα(2n, n)/log n) processors per update and O(logn) time using O(nα(2n, n)/logn) processors per query, where α(.,.) is the inverse of Ackermann's function. (3) An NC algorithm for the triconnectivity problem is also derived, which takes O(log n log_n/^m+logn log log n/α(3n, n)) time using O(nα(3n, n)/log n) processors per update and O(1) time with a single processor per query. (4) An NC algorithm for the 3-edge connectivity problem is obtained, which has the same time and processor complexities as the algorithm for the triconnectivity problem. To the best of our knowledge, the proposed algorithms are the first NC algorithms for the problems using O(n) processors in contrast to Ω(m) processors for solving them from scratch. In particular, the proposed NC algorithm for the 2-edge connectivity problem uses only O(n^3/4) processors. All the proposed algorithms run on a CRCW PRAM     

Efficient geometric algorithms on the EREW PRAM

We present a technique that can be used to obtain efficient parallel geometric algorithms in the EREW PRAM computational model. This technique enables us to solve optimally a number of geometric problems in O(log n) time using O(n/log n) EREW PRAM processors, where n is the input size of a problem. These problems include: computing the convex hull of a set of points in the plane that are given sorted, computing the convex hull of a simple polygon, computing the common intersection of half-planes whose slopes are given sorted, finding the kernel of a simple polygon, triangulating a set of points in the plane that are given sorted, triangulating monotone polygons and star-shaped polygons, and computing the all dominating neighbors of a sequence of values. PRAM algorithms for these problems were previously known to be optimal (i.e., in O(log n) time and using O(n/log n) processors) only on the CREW PRAM, which is a stronger model than the EREW PRAM     

An efficient parallel strategy for the two-fixed-endpoint Hamiltonian path problem on distance-hereditary graphs

In this paper, we solve the two-fixed-endpoint Hamiltonian path problem on distance-hereditary graphs efficiently in parallel. Let T_d(|V|,|E|) and P_d(|V|,|E|) denote the parallel time and processor complexities, respectively, required to construct a decomposition tree of a distance-hereditary graph G=(V,E) on a PRAM model M_d. We show that this problem can be solved in O(T_d(|V|,|E|)+log|V|) time using O(P_d(|V|,|E|)+(|V|+|E|)/log|V|) processors on M_d. Moreover, if G is represented by its decomposition tree form, the problem can be solved optimally in O(log|V|) time using O((|V|+|E|)/log|V|) processors on an EREW PRAM. We also obtain a linear-time algorithm which is faster than the previous known O(|V|³) sequential algorithm.     

Parallel Algorithms for Maximum Matching in Complements of Interval Graphs and Related Problems

Given a set of n intervals representing an interval graph, the problem of finding a maximum matching between pairs of disjoint (nonintersecting) intervals has been considered in the sequential model. In this paper we present parallel algorithms for computing maximum cardinality matchings among pairs of disjoint intervals in interval graphs in the EREW PRAM and hypercube models. For the general case of the problem, our algorithms compute a maximum matching in O( log ³ n) time using O(n/ log ² n) processors on the EREW PRAM and using n processors on the hypercubes. For the case of proper interval graphs, our algorithm runs in O( log n ) time using O(n) processors if the input intervals are not given already sorted and using O(n/ log n ) processors otherwise, on the EREW PRAM. On n -processor hypercubes, our algorithm for the proper interval case takes O( log n log log n ) time for unsorted input and O( log n ) time for sorted input. Our parallel results also lead to optimal sequential algorithms for computing maximum matchings among disjoint intervals. In addition, we present an improved parallel algorithm for maximum matching between overlapping intervals in proper interval graphs. Received November 20, 1995; revised September 3, 1998.     

Efficient parallel binary search on sorted arrays, withapplications

Let A be a sorted array of n numbers and B a sorted array of m numbers, both in nondecreasing order, with n⩽m. We consider the problem of determining, for each element A(j), j=1, 2, …, n, the unique element B(i), 0⩽i⩽m, such that B(i)⩽A(j)相似文献   

Fast Generation of Random Permutations Via Networks Simulation

We consider the problem of generating random permutations with uniform distribution. That is, we require that for an arbitrary permutation π of n elements, with probability 1/n! the machine halts with the i th output cell containing π(i) , for 1 ≤ i ≤ n . We study this problem on two models of parallel computations: the CREW PRAM and the EREW PRAM. The main result of the paper is an algorithm for generating random permutations that runs in O(log log n) time and uses O(n ^1+o(1) ) processors on the CREW PRAM. This is the first o(log n) -time CREW PRAM algorithm for this problem. On the EREW PRAM we present a simple algorithm that generates a random permutation in time O(log n) using n processors and O(n) space. This algorithm outperforms each of the previously known algorithms for the exclusive write PRAMs. The common and novel feature of both our algorithms is first to design a suitable random switching network generating a permutation and then to simulate this network on the PRAM model in a fast way. Received November 1996; revised March 1997.     

完全欧几里德距离变换的最优算法

欧几里德距离变换（ＥＤＴ）对由黑白素构成的二值图象中所有象素找出其到最近黑素的距离，应用于图象分析，计算机视觉，在本文之前，该问题的最好复杂度为Ｏ（ｎ＾２ｌｏｇｎ）。本文提出了一个复杂度为Ｏ（ｎ＾２）的算法，使复杂度达到最优，该算法可以并行化，在有ｒ个处理单元的ＥＲＥＷＰＲＡＭ计算模型上，若ｒｌｏｇｒ≤２２／６ｎ，则时间复杂度为Ｏ（ｎ／ｒ）否则为Ｏ（ｎｌｏｇｒ）。     

Finding least-weight subsequences with fewer processors

By restricting weight functions to satisfy the quadrangle inequality or the inverse quadrangle inequality, significant progress has been made in developing efficient sequential algorithms for the least-weight subsequence problem [10], [9], [12], [16]. However, not much is known on the improvement of the naive parallel algorithm for the problem, which is fast but demands too many processors (i.e., it takesO(log² n) time on a CREW PRAM with n³/logn processors). In this paper we show that if the weight function satisfies the inverse quadrangle inequality, the problem can be solved on a CREW PRAM in O(log² n log logn) time withn/log logn processors, or in O(log² n) time withn logn processors. Notice that the processor-time complexity of our algorithm is much closer to the almost linear-time complexity of the best-known sequential algorithm [12].     

Optimal Sublogarithmic Time Parallel Algorithms on Rooted Forests

The problem of finding a sublogarithmic time optimal parallel algorithm for 3 -colouring rooted forests has been open for long. We settle this problem by obtaining an O(( log log n) log ^* ( log ^* n)) time optimal parallel algorithm on a TOLERANT Concurrent Read Concurrent Write (CRCW) Parallel Random Access Machine (PRAM). Furthermore, we show that if f(n) is the running time of the best known algorithm for 3 -colouring a rooted forest on a COMMON or TOLERANT CRCW PRAM, a fractional independent set of the rooted forest can be found in O(f(n)) time with the same number of processors, on the same model. Using these results, it is shown that decomposable top-down algebraic computation and, hence, depth computation (ranking), 2 -colouring and prefix summation on rooted forests can be done in O( log n) optimal time on a TOLERANT CRCW PRAM. These algorithms have been obtained by proving a result of independent interest, one concerning the self-simulation property of TOLERANT: an N -processor TOLERANT CRCW PRAM that uses an address space of size O(N) only, can be simulated on an n -processor TOLERANT PRAM in O(N/n) time, with no asymptotic increase in space or cost, when n=O(N/ log log N) . Received May 20, 1997; revised June 15, 1998.     

An Optimal Parallel Co-Connectivity Algorithm

In this paper we consider the problem of computing the connected components of the complement of a given graph. We describe a simple sequential algorithm for this problem, which works on the input graph and not on its complement, and which for a graph on n vertices and m edges runs in optimal O(n+m) time. Moreover, unlike previous linear co-connectivity algorithms, this algorithm admits efficient parallelization, leading to an optimal O(log n)-time and O((n+m)log n)-processor algorithm on the EREW PRAM model of computation. It is worth noting that, for the related problem of computing the connected components of a graph, no optimal deterministic parallel algorithm is currently available. The co-connectivity algorithms find applications in a number of problems. In fact, we also include a parallel recognition algorithm for weakly triangulated graphs, which takes advantage of the parallel co-connectivity algorithm and achieves an O(log² n) time complexity using O((n+m²) log n) processors on the EREW PRAM model of computation.     

Recognizing unordered depth-first search trees of an undirectedgraph in parallel

Let G be an undirected graph and T be a spanning tree of G. In this paper, an efficient parallel algorithm is proposed for determining whether T is an unordered depth-first search tree of G. The proposed algorithm runs in O(m/p+log m) time using p processors on the EREW PRAM, where m is the number of edges contained in G. It is cost-optimal and achieves linear speedup     

Constant Time Boolean Matrix Multiplication on a Linear Array with a Reconfigurable Pipelined Bus System

We show that the product of two N × N boolean matrices can be calculated in constant time on an LARPBS with O(N3 / log N) processors. All data communications and computations are performed on the bit level. To the best of the author's knowledge, this is the first parallel boolean matrix multiplication algorithm that has constant execution time, and is executed on a distributed memory system with (N3) processors. By using our boolean matrix multiplication algorithm, it is shown that the transitive closure of a directed graph can be obtained in O(log N) time ( measured by bit level operations) on an LARPBS with O (N3 / log N) processors. To the best of our knowledge, this is the first parallel algorithm for tansitive closure of directed graphs with time complexity O(log N) (comparable to that of CRCW PRAM) and cost O (N3) on a realistic parallel computing model, which has no shared memory, and interprocessor communications are dealt with explicitly and efficiently.     

Optimal Sequential And Parallel Algorithms To Compute A Steiner Tree On Permutation Graphs

This paper presents an optimal sequential and an optimal parallel algorithm to compute a minimum cardinality Steiner set and a Steiner tree. The sequential algorithm takes O ( n ) time and parallel algorithm takes O (log n ) time and O ( n /log n ) processors on an EREW PRAM model.     

A simple parallel algorithm to draw cubic graphs

The main contribution of this work is to offer a simple and cost-efficient parallel algorithm that, given an arbitrary n-vertex cubic graph G as input, produces an orthogonal grid drawing of G in O(log n) time, using n processors on an EREW PRAM. Our algorithm matches the time and cost performance of the best previously-known algorithm while at the same time improving the constant factors involved in two important metrics: layout area and number of bends. More importantly, however, our algorithm stands out by its conceptual simplicity and ease of implementation.     

Efficient EREW PRAM algorithms for parentheses-matching

We present four polylog-time parallel algorithms for matching parentheses on an exclusive-read and exclusive-write (EREW) parallel random-access machine (PRAM) model. These algorithms provide new insights into the parentheses-matching problem. The first algorithm has a time complexity of O(log² n) employing O(n/(log n)) processors for an input string containing n parentheses. Although this algorithm is not cost-optimal, it is extremely simple to implement. The remaining three algorithms, which are based on a different approach, achieve O(log n) time complexity in each case, and represent successive improvements. The second algorithm requires O(n) processors and working space, and it is comparable to the first algorithm in its ease of implementation. The third algorithm uses O(n/(log n)) processors and O(n log n) space. Thus, it is cost-optimal, but uses extra space compared to the standard stack-based sequential algorithm. The last algorithm reduces the space complexity to O(n) while maintaining the same processor and time complexities. Compared to other existing time-optimal algorithms for the parentheses-matching problem that either employ extensive pipelining or use linked lists and comparable data structures, and employ sorting or a linked list ranking algorithm as subroutines, the last two algorithms have two distinct advantages. First, these algorithms employ arrays as their basic data structures, and second, they do not use any pipelining, sorting, or linked list ranking algorithms     

Reconstructing a Binary Tree from Its Traversals in Doubly Logarithmic CREW Time

We consider the following problem. For a binary tree T = (V, E) where V = {1, 2, ..., n}, given its inorder traversal and either its preorder or its postorder traversal, reconstruct the binary tree. We present a new parallel algorithm for this problem. Our algorithm requires O(n) space. The main idea of our algorithm is to reduce the reconstruction process to merging two sorted sequences. With the best parallel merging algorithms, our algorithm can be implemented in O(log log n) time using O(n/log log n) processors on the CREW PRAM (or in O(log n) time using O(n/log n) processors on the EREW PRAM). Our result provides one more example of a fundamental problem which can be solved by optimal parallel algorithms in O(log log n)time on the CREW PRAM.     

Parallel algorithms for relational coarsest partition problems

Relational Coarsest Partition Problems (RCPPs) play a vital role in verifying concurrent systems. It is known that RCPPs are P-complete and hence it may not be possible to design polylog time parallel algorithms for these problems. In this paper, we present two efficient parallel algorithms for RCPP in which its associated label transition system is assumed to have m transitions and n states. The first algorithm runs in O(n^1+ϵ) time using m/n^ϵ CREW PRAM processors, for any fixed ϵ<1. This algorithm is analogous to and optimal with respect to the sequential algorithm of P.C. Kanellakis and S.A. Smolka (1990). The second algorithm runs in O(n log n) time using m/n CREW PRAM processors. This algorithm is analogous to and nearly optimal with respect to the sequential algorithm of R. Paige and R.E. Tarjan (1987)     

More Efficient Topological Sort Using Reconfigurable Optical Buses

Topological sort of an acyclic graph has many applications such as job scheduling and network analysis. Due to its importance, it has been tackled on many models. Dekel et al. [3], proposed an algorithm for solving the problem in O(log² N) time on the hypercube or shuffle-exchange networks with O(N ³) processors. Chaudhuri [2], gave an O(log N) algorithm using O(N ³) processors on a CRCW PRAM model. On the LARPBS (Linear Arrays with a Reconfigurable Pipelined Bus System) model, Li et al. [5] showed that the problem for a weighted directed graph with N vertices can be solved in O(log N) time by using N ³ processors. In this paper, a more efficient topological sort algorithm is proposed on the same LARPBS model. We show that the problem can be solved in O(log N) time by using N ³/log N processors. We show that the algorithm has better time and processor complexities than the best algorithm on the hypercube, and has the same time complexity but better processor complexity than the best algorithm on the CRCW PRAM model.