Efficient Parallel Algorithms for Planar st-Graphs

Planar st -graphs find applications in a number of areas. In this paper we present efficient parallel algorithms for solving several fundamental problems on planar st -graphs. The problems we consider include all-pairs shortest paths in weighted planar st -graphs, single-source shortest paths in weighted planar layered digraphs (which can be reduced to single-source shortest paths in certain special planar st -graphs), and depth-first search in planar st -graphs. Our parallel shortest path techniques exploit the specific geometric and graphic structures of planar st -graphs, and involve schemes for partitioning planar st -graphs into subgraphs in a way that ensures that the resulting path length matrices have a monotonicity property [1], [2]. The parallel algorithms we obtain are a considerable improvement over the previously best known solutions (when they are applied to these st -graph problems), and are in fact relatively simple. The parallel computational models we use are the CREW PRAM and EREW PRAM.     

Finding a k-tree core and a k-tree center of a tree network inparallel

A k-tree core of a tree network is a subtree with exactly k leaves that minimizes the total distance from vertices to the subtree. A k-tree center of a tree network is a subtree with exactly k leaves that minimizes the distance from the farthest vertex to the subtree. In this paper, two efficient parallel algorithms are proposed for finding a k-tree core and a k-tree center of a tree network, respectively. Both the proposed algorithms perform on the EREW PRAM in O(log n log n) time using O(n) work (time-processor product). Besides being efficient on the EREW PRAM, in the sequential case, our algorithm for finding a k-tree core of a tree network improves the two algorithms previously proposed     

Efficient Parallel Algorithms for Planar st -Graphs

   Abstract. Planar st -graphs find applications in a number of areas. In this paper we present efficient parallel algorithms for solving several fundamental problems on planar st -graphs. The problems we consider include all-pairs shortest paths in weighted planar st -graphs, single-source shortest paths in weighted planar layered digraphs (which can be reduced to single-source shortest paths in certain special planar st -graphs), and depth-first search in planar st -graphs. Our parallel shortest path techniques exploit the specific geometric and graphic structures of planar st -graphs, and involve schemes for partitioning planar st -graphs into subgraphs in a way that ensures that the resulting path length matrices have a monotonicity property [1], [2]. The parallel algorithms we obtain are a considerable improvement over the previously best known solutions (when they are applied to these st -graph problems), and are in fact relatively simple. The parallel computational models we use are the CREW PRAM and EREW PRAM.     

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 algorithm for the efficient domination problem on distance-hereditary graphs

In the literature, there are quite a few sequential and parallel algorithms for solving problems on distance-hereditary graphs. With an n-vertex and m-edge distance-hereditary graph G, we show that the efficient domination problem on G can be solved in O(log/sup 2/ n) time using O(n + m) processors on a CREW PRAM. Moreover, if a binary tree representation of G is given, the problem can be optimally solved in O(log n) time using O(n/log n) processors on an EREW PRAM.     

Finding r-dominating sets and p-centers of trees in parallel

Let T=(V, E) be an edge-weighted tree with |V|=n vertices embedded in the Euclidean plane. Let IE denote the set of all points on the edges of T. Let X and Y be two subsets of IE and let r be a positive real number. A subset D/spl sube/X is an X/Y/r-dominating set if every point in Y is within distance r of a point in D. The X/Y/r-dominating set problem is to find an X/Y/r-dominating set D* with minimum cardinality. Let p/spl ges/1 be an integer. The X/Y/p-center problem is to find a subset C*/spl sube/X of p points such that the maximum distance of any point in Y from C* is minimized. Let X and Y be either V or IE. In this paper, efficient parallel algorithms on the EREW PRAM are first presented for the X/Y/r-dominating set problem. The presented algorithms require O(log/sup 2/n) time for all cases of X and Y. Parallel algorithms on the EREW PRAM are then developed for the X/Y/p-center problem. The presented algorithms require O(log/sup 3/n) time for all cases of X and Y. Previously, sequential algorithms for these two problems had been extensively studied in the literature. However, parallel solutions with polylogarithmic time existed only for their special cases. The algorithms presented in this paper are obtained by using an interesting approach which we call the dependency-tree approach. Our results are examples of parallelizing sequential dynamic-programming algorithms by using the approach.     

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     

Optimal parallel algorithms for finding proximate points, withapplications

Consider a set P of points in the plane sorted by the x-coordinate. A point p in P is said to be a proximate point if there exists a point q on the x-axis such that p is the closest point to q over all points in P. The proximate point problem is to determine all the proximate points in P. Our main contribution is to propose optimal parallel algorithms for solving instances of size n of the proximate points problem. We begin by developing a work-time optimal algorithm running in O(log log n) time and using n/loglogn Common-CRCW processors. We then go on to show that this algorithm can be implemented to run in O(log n) time using n/logn EREW processors. In addition to being work-time optimal, our EREW algorithm turns out to also be time-optimal. Our second main contribution is to show that the proximate points problem finds interesting, and quite unexpected, applications to digital geometry and image processing. As a first application, we present a work-time optimal parallel algorithm for finding the convex hull of a set of n points in the plane sorted by x-coordinate; this algorithm runs in O(log log n) time using n/logn Common-CRCW processors. We then show that this algorithm can be implemented to run in O(log n) time using n/logn EREW processors. Next, we show that the proximate points algorithms afford us work-time optimal (resp, time-optimal) parallel algorithms for various fundamental digital geometry and image processing problems     

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.     

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     

A parallel algorithm for constructing a labeled tree

A tree T is labeled when the n vertices are distinguished from one another by names such as v₁, v₂…v_n . Two labeled trees are considered to be distinct if they have different vertex labels even though they might be isomorphic. According to Cayley's tree formula, there are n^n-2 labeled trees on n vertices. Prufer used a simple way to prove this formula and demonstrated that there exists a mapping between a labeled tree and a number sequence. From his proof, we can find a naive sequential algorithm which transfers a labeled tree to a number sequence and vice versa. However, it is hard to parallelize. In this paper, we shall propose an O(log n) time parallel algorithm for constructing a labeled tree by using O(n) processors and O(n log n) space on the EREW PRAM computational model     

Spanning trees and shortest paths in monge graphs

We investigate three problems onMonge graphs, i.e. complete, undirected weighted graphs whose distance matrix is a Monge matrix: (A) the minimum spanning tree problem, (B) the problem of computing all-pairs shortest paths and (C) the problem of determining a minimum weighted 1-to-all shortest path tree. For all three problems best possible algorithms (in terms of complexity) are presented. This research has been supported by the Spezialforschungsbereich F 003 ‘Optimierung und Kontrolle’/Projektbereich Diskrete Optimierung.     

Parallel Algorithms for Series Parallel Graphs and Graphs with Treewidth Two 1

In this paper a parallel algorithm is given that, given a graph G=(V,E) , decides whether G is a series parallel graph, and, if so, builds a decomposition tree for G of series and parallel composition rules. The algorithm uses O(log \kern -1pt |E|log ^\ast \kern -1pt |E|) time and O(|E|) operations on an EREW PRAM, and O(log \kern -1pt |E|) time and O(|E|) operations on a CRCW PRAM. The results hold for undirected as well as for directed graphs. Algorithms with the same resource bounds are described for the recognition of graphs of treewidth two, and for constructing tree decompositions of treewidth two. Hence efficient parallel algorithms can be found for a large number of graph problems on series parallel graphs and graphs with treewidth two. These include many well-known problems like all problems that can be stated in monadic second-order logic. Received July 15, 1997; revised January 29, 1999, and June 23, 1999.     

A Randomized Linear-Work EREW PRAM Algorithm to Find a Minimum Spanning Forest

We present a randomized EREW PRAM algorithm to find a minimum spanning forest in a weighted undirected graph. On an n -vertex graph the algorithm runs in o(( log n)1+?) expected time for any ? >0 and performs linear expected work. This is the first linear-work, polylog-time algorithm on the EREW PRAM for this problem. This also gives parallel algorithms that perform expected linear work on two general-purpose models of parallel computation—the QSM and the BSP.     

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.     

不可达顶点剪枝算法及其在最短路径中的应用

[k]步可达性查询用于回答图[G]中从顶点[u]到达顶点[v]最多[k]步是否存在路径,但其多用于无权图的可达性研究。针对加权图,在图中构建了最早到达、逆向最早到达和最晚到达等三个索引,并应用这三个索引实现对不可达顶点的快速剪枝,从而有效地缩减了加权图的规模。运用该方法建立索引并剪枝顶点的时间复杂度与空间复杂度分别为[O(n+e)]和[O(n)],这里[n]和[e]分别为图中顶点的数目和边的数目。该方法可以与Dijkstra算法、Floyd算法和A*算法等多种传统算法相结合,并应用于最短路径求解,从而提高传统算法计算性能。最后以物流配送网络为例进行了实验验证,实验结果表明提出的方法可以正确并高效地对不必要计算的顶点进行剪枝,从而加快了最短路径求解速度,验证了提出方法的有效性。     

Efficient parallel term matching and anti-unification

In this paper we present optimal processor x time parallel algorithms for term matching and anti-unification of terms represented as trees. Term matching is the special case of unification in which one of the terms is restricted to contain no variables. It has wide applicability to logic programming, term rewriting systems and symbolic pattern matching. Anti-unification is the dual problem of unification in which one computes the most specific generalization of two terms. It has application to inductive inference and theorem proving. Our algorithms run in O(log² N) time using N/log² N processors on a shared-memory model of computation that prohibits simultaneous reads or writes (EREW PRAM). These algorithms are the first polylogarithmic-time EREW algorithms with a processor x time product of the same order as that of their sequential counterparts, thereby permitting optimal speed-ups using any number of processors up to N/log² N. We also use the techniques developed in the paper to provide an N/log N-processor, O(log N)-time algorithm for a shared-memory model that allows both simultaneous reads and simultaneous writes (CRCW PRAM).Supported by NSF Grant IRI-88-09324 and NSF/DARPA Grant CCR-8908092.     

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     

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.     

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.