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.     

Parallel breadth-first search algorithms for trees and graphs

Parallel Breadth-First Search (BFS) algorithms for ordered trees and graphs on a shared memory model of a Single Instruction-stream Multiple Data-stream computer are proposed. The parallel BFS algorithm for trees computes the BFS rank of eachnode of an ordered tree consisting of n nodes in time of 0(β log n) when 0(n ^1+1/β) processors are used, β being an integer greater than or equal to 2. The parallel BFS algorithm for graphs produces Breadth-First Spanning Trees (BFSTs) of a directedgraph G having n nodes in time 0(log d.log n) using 0(n ³) processors, where d is the diameter of G If G is a strongly connected graph or a connected undirected graph the BFS algorithm produces n BFSTs, each BFST having a different start node.     

An optimal speed-up parallel algorithm for triangulating simplicial point sets in space

Previous research on developing parallel triangulation algorithms concentrated on triangulating planar point sets.O(log³ n) running time algorithms usingO(n) processors have been developed in Refs. 1 and 2. Atallah and Goodrich⁽³⁾ presented a data structure that can be viewed as a parallel analogue of the sequential plane-sweeping paradigm, which can be used to triangulate a planar point set inO(logn loglogn) time usingO(n) processors. Recently Merks⁽⁴⁾ described an algorithm for triangulating point sets which runs inO(logn) time usingO(n) processors, and is thus optimal. In this paper we develop a parallel algorithm for triangulating simplicial point sets in arbitrary dimensions based on the idea of the sequential algorithm presented in Ref. 5. The algorithm runs inO(log² n) time usingO(n/logn) processors. The algorithm hasO(n logn) as the product of the running time and the number of processors; i.e., an optimal speed-up.     

Parallel algorithms for minimal spanning trees of directed graphs

The main results of this paper are efficient parallel algorithms, MSP and LOCATE, for computing minimal spanning trees and locating minimal paths in directed graphs, respectively. Algorithm MSP has time complexityO(log³ n) usingO(n ³/logn) processors, while LOCATE has time complexityO(logn) usingO(n ²) processors. Algorithm MSP is derived from sequential algorithms, when the unbounded parallelism model is used.     

Parallel algorithms for arrangements

We give the first efficient parallel algorithms for solving the arrangement problem. We give a deterministic algorithm for the CREW PRAM which runs in nearly optimal bounds ofO (logn log^* n) time andn ²/logn processors. We generalize this to obtain anO (logn log^* n)-time algorithm usingn ^d /logn processors for solving the problem ind dimensions. We also give a randomized algorithm for the EREW PRAM that constructs an arrangement ofn lines on-line, in which each insertion is done in optimalO (logn) time usingn/logn processors. Our algorithms develop new parallel data structures and new methods for traversing an arrangement.This work was supported by the National Science Foundation, under Grants CCR-8657562 and CCR-8858799, NSF/DARPA under Grant CCR-8907960, and Digital Equipment Corporation. A preliminary version of this paper appeared at the Second Annual ACM Symposium on Parallel Algorithms and Architectures [3].     

Approximate algorithms for the knapsack problem on parallel computers

Computing an optimal solution to the knapsack problem is known to be NP-hard. Consequently, fast parallel algorithms for finding such a solution without using an exponential number of processors appear unlikely. An attractive alternative is to compute an approximate solution to this problem rapidly using a polynomial number of processors. In this paper, we present an efficient parallel algorithm for finding approximate solutions to the 0–1 knapsack problem. Our algorithm takes an , 0 < < 1, as a parameter and computes a solution such that the ratio of its deviation from the optimal solution is at most a fraction of the optimal solution. For a problem instance having n items, this computation uses O(n^5/2/^3/2) processors and requires O(log³n + log²nlog(1/)) time. The upper bound on the processor requirement of our algorithm is established by reducing it to a problem on weighted bipartite graphs. This processor complexity is a significant improvement over that of other known parallel algorithms for this problem.     

Efficient parallel solutions to some geometric problems

This paper presents new algorithms for solving some geometric problems on a shared memory parallel computer, where concurrent reads are allowed but no two processors can simultaneously attempt to write in the same memory location. The algorithms are quite different from known sequential algorithms, and are based on the use of a new parallel divide-and-conquer technique. One of our results is an O(log n) time, O(n) processor algorithm for the convex hull problem. Another result is an O(log n log log n) time, O(n) processor algorithm for the problem of selecting a closest pair of points among n input points.     

Parallel algorithms for line generation

A new, parallel approach for generating Bresenham-type lines is developed. Coordinate pairs which approximate straight lines on a square grid are derived from line equations. These pairs serve as a basis for the development of four new parallel algorithms. One of the algorithms uses the fact that straight time generation is equivalent to a vector prefix sums calculation. The algorithms execute on a binary tree of processors. Each node in the tree performs a simple calculation that involves only additions and shifts. All four algorithms have time complexityO(log₂ n) wheren in the form 2 ^m denotes the number of points generated andn-1 is the number of processors in the tree. This compares toO(n) for Bresenham's algorithm executed on a sequential processor. Pipelining can be used to achieve a constant time per line generation as long as line length is less thann.     

Complex polynomials and circuit lower bounds for modular counting

We study the power of constant-depth circuits containing negation gates, unbounded fan-in AND and OR gates, and a small number of MAJORITY gates. It is easy to show that a depth 2 circuit of sizeO(n) (wheren is the number of inputs) containingO(n) MAJORITY gates can determine whether the sum of the input bits is divisible byk, for any fixedk>1, whereas it is known that this requires exponentialsize circuits if we have no MAJORITY gates. Our main result is that a constant-depth circuit of size containingn ^o(1) MAJORITY gates cannot determine if the sum of the input bits is divisible byk; moreover, such a circuit must give the wrong answer on a constant fraction of the inputs. This result was previously known only fork=2. We prove this by obtaining an approximate representation of the behavior of constant-depth circuits by multivariate complex polynomials.     

An NC algorithm for finding a minimum weighted completion time schedule on series parallel graphs

We present a parallel algorithm for solving the minimum weighted completion time scheduling problem for transitive series parallel graphs. The algorithm takesO(log² n) time withO(n ³) processors on a CREW PRAM, wheren is the number of vertices of the input graph. This is the first NC algorithm for solving the problem.Research supported in part by NSF Grants CCR-9011214 and CCR-9205982.     

PRAM和LARPBS模型上有向序列翻转距离并行算法

分别在两种重要并行计算模型中给出计算有向基因组排列的反转距离新的并行算法.基于Hannenhalli和Pevzner理论,分3个主要部分设计并行算法:构建断点图、计算断点图中圈数、计算断点图中障碍的数目.在CREW-PRAM模型上,算法使用O(n²)处理器,时间复杂度为O(log²n);在基于流水光总线的可重构线性阵列系统(linear array with a reconfigurable pipelined bus system, LARPBS)模型上,算法使用O(n³)处理器,计算时间复杂度为O(logn).     

Simulating Boolean Circuits on a DNA Computer

We demonstrate that DNA computers can simulate Boolean circuits with a small overhead. Boolean circuits embody the notion of massively parallel signal processing and are frequently encountered in many parallel algorithms. Many important problems such as sorting, integer arithmetic, and matrix multiplication are known to be computable by small size Boolean circuits much faster than by ordinary sequential digital computers. This paper shows that DNA chemistry allows one to simulate large semi-unbounded fan-in Boolean circuits with a logarithmic slowdown in computation time. Also, for the class NC ¹ , the slowdown can be reduced to a constant. In this algorithm we have encoded the inputs, the Boolean AND gates, and the OR gates to DNA oligonucleotide sequences. We operate on the gates and the inputs by standard molecular techniques of sequence-specific annealing, ligation, separation by size, amplification, sequence-specific cleavage, and detection by size. Additional steps of amplification are not necessary for NC ¹ circuits. The feasibility of the DNA algorithm has been successfully tested on a small circuit by actual biochemical experiments. Received May 29, 1997; revised February 15, 1998.     

Parallel Algorithms for Maximal Acyclic Sets

Given a graph G=(V,E), the well-known spanning forest problem of G can be viewed as the problem of finding a maximal subset F of edges in G such that the subgraph induced by F is acyclic. Although this problem has well-known efficient NC algorithms, its vertex counterpart, the problem of finding a maximal subset U of vertices in G such that the subgraph induced by U is acyclic, has not been shown to be in NC (or even in RNC) and is not believed to be parallelizable in general. In this paper we present NC algorithms for solving the latter problem for two special cases. First, we show that, for a planar graph with n vertices, the problem can be solved in time with O(n) processors on an EREW PRAM. Second, we show that the problem is solvable in NC if the input graph G has only vertex-induced paths of length polylogarithmic in the number of vertices of G. As a consequence of this result, we show that certain natural extensions of the well-studied maximal independent set problem remain solvable in NC. Moreover, we show that, for a constant-degree graph with n vertices, the problem can be solved in time with O(n ² ) processors on an EREW PRAM. Received July 3, 1995; revised April 1, 1996.     

Parallel approximation schemes for Subset Sum and Knapsack problems

Summary This paper presents parallel approximation schemes for the Subset Sum, 0–1 Knapsack, and several other optimization problems. These algorithms offer a three-way trade-off among parallel time, the accuracy of the solution, and the number of processors used. The maximum numbers of processors which can be usefully employed depend on n (the size of the input), and the accuracy requirement . The parallel running times of the algorithms are polynomial in both log n and log(1/) when enough processors are used.Parts of this research were done while both authors were at the Department of Computer Science, University of Toronto     

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].     

An optimal parallel algorithm for planar cycle separators

We present an optimal parallel algorithm for computing a cycle separator of ann-vertex embedded planar undirected graph inO(logn) time onn/logn processors. As a consequence, we also obtain an improved parallel algorithm for constructing a depth-first search tree rooted at any given vertex in a connected planar undirected graph in O(log² n) time on n/logn processors. The best previous algorithms for computing depth-first search trees and cycle separators achieved the same time complexities, but withn processors. Our algorithms run on a parallel random access machine that permits concurrent reads and concurrent writes in its shared memory and allows an arbitrary processor to succeed in case of a write conflict.A preliminary version of this paper appeared as Improved Parallel Depth-First Search in Undirected Planar Graphs in theProceedings of the Third Workshop on Algorithms and Data Structures, 1993, pp. 407–420.Supported in part by NSF Grant CCR-9101385.     

Efficient parallel algorithms for breadth first spanning forests of general graphs

Ghosh and Bhattacharjee propose [2] (Intern. J. Computer Math., 1984, Vol. 15, pp. 255-268) an algorithm of determining breadth first spanning trees for graphs, which requires that the input graphs contain some vertices, from which every other vertex in the input graph can be reached. These vertices are called starting vertices. The complexity of the GB algorithm is O(log² n) using O{n ³) processors. In this paper an algorithm, named BREADTH, also computing breadth first spanning trees, is proposed. The complexity is O(log² n) using O{n ³/logn) processors. Then an efficient parallel algorithm, named- BREADTHFOREST, is proposed, which generalizes algorithm BREADTH. The output of applying BREADTHFOREST to a general graph, which may not contain any starting vertices, is a breadth first spanning forest of the input graph. The complexity of BREADTHFOREST is the same as BREADTH.     

Efficient Parallel Computation of the Characteristic Polynomial of a Sparse, Separable Matrix

{This paper is concerned with the problem of computing the characteristic polynomial of a matrix. In a large number of applications, the matrices are symmetric and sparse : with O(n) non-zero entries. The problem has an efficient sequential solution in this case, requiring O(n ² ) work by use of the sparse Lanczos method. A major remaining open question is: to find a polylog time parallel algorithm with matching work bounds. Unfortunately, the sparse Lanczos method cannot be parallelized to faster than time Ω (n) using n processors. Let M(n) be the processor bound to multiply two n \times n matrices in O(log n) parallel time. Giesbrecht [G2] gave the best previous polylog time parallel algorithms for the characteristic polynomial of a dense matrix with O (M(n)) processors. There is no known improvement to this processor bound in the case where the matrix is sparse. Often, in addition to being symmetric and sparse, the matrix has a sparsity graph (which has edges between indices of the matrix with non-zero entries) that has small separators. This paper gives a new algorithm for computing the characteristic polynomial of a sparse symmetric matrix, assuming that the sparsity graph is s(n) -separable and has a separator of size s(n)=O(n ^γ ) , for some γ , 0 < γ < 1 , that when deleted results in connected components of ≤α n vertices, for some 0 < α < 1 , with the same property. We derive an interesting algebraic version of Nested Dissection, which constructs a sparse factorization of the matrix A-λ I _n where A is the input matrix and I _n is the n \times n identity matrix. While Nested Dissection is commonly used to minimize the fill-in in the solution of sparse linear systems, our innovation is to use the separator structure to bound also the work for manipulation of rational functions in the recursively factored matrices. The matrix elements are assumed to be over an arbitrary field. We compute the characteristic polynomial of a sparse symmetric matrix in polylog time using P(n)(n+M(s(n))) ≤ P(n)(n+ s(n) ^2.376 ) processors, where P(n) is the processor bound to multiply two degree n polynomials in O(log n) parallel time using a PRAM (P(n) = O(n) if the field supports an FFT of size n but is otherwise O(nlog log n) [CK]. Our method requires only that a matrix be symmetric and non-singular (it need not be positive definite as usual for Nested Dissection techniques). For the frequently occurring case where the matrix has small separator size, our polylog parallel algorithm has work bounds competitive with the best known sequential algorithms (i.e., the Ω(n ² ) work of sparse Lanczos methods), for example, when the sparsity graph is a planar graph, s(n) ≤ O( \sqrt n ) , and we require polylog time with only P(n)n ^1.188 processors. } Received September 26, 1997; revised June 5, 1999.     

An efficient all-parses systolic algorithm for general context-free parsing

The problem of outputting all parse trees of a string accepted by a context-free grammar is considered. A systolic algorithms is presented that operates inO(m·n) time, wherem is the number of distinct parse trees andn is the length of the input. The systolic array usesn ² processors, each of which requires at mostO(logn) bits of storage. This is much more space-efficient that a previously reported systolic algorithm for the same problem, which requiredO(n logn) space per processor. The algorithm also extends previous algorithms that only output a single parse tree of the input.Research squpported in part by NSF Grant DCR-8420935 and DCR-8604603.