Parallel triangulation of a polygon in two calls to the trapezoidal map

We give a parallel method for triangulating a simple polygon by two (parallel) calls to the trapezoidal map computation. The method is simpler and more elegant than previous methods. Along the way we obtain an interesting partition of one-sided monotone polygons. Using the best-known trapezoidal map algorithm, ours run in timeO(logn) usingO(n) CREW PRAM processors.This research was supported by NSF Grants No. DCR-84-01898 and No. DCR-84-01633, and ONR Contract N00014-85-K-0046.     

Convex hulls of objects bounded by algebraic curves

We present an0(n ·d ^o(1)) algorithm to compute the convex hull of a curved object bounded by0(n) algebraic curve segments of maximum degreed.Research supported in part by NSF Grant MIP-85 21356, ARO Contract DAA G29-85-C0018 under Cornell MSI, and ONR Contract N00014-88-K-0402. This paper is an updated version of a part of [6].     

A parallel algorithm for approximating the minimum cycle cover

We address the problem of approximating aminimum cycle cover in parallel. We give the first efficient parallel algorithm for finding an approximation to aminimum cycle cover. Our algorithm finds a cycle cover whose size is within a factor of 0(1 +n logn/(m + n) of the minimum-sized cover usingO(log² n) time on (m + n)/logn processors.Research supported by ONR Grant N00014-88-K-0243 and DARPA Grant N00039-88-C0113 at Harvard University.Research supported by a graduate fellowship from GE. Additional support provided by Air Force Contract AFOSR-86-0078, and by an NSF PYI awarded to David Shmoys, with matching funds from IBM, Sun Microsystems, and UPS.     

Optimal parallel algorithms for point-set and polygon problems

In this paper we give parallel algorithms for a number of problems defined on point sets and polygons. All our algorithms have optimalT(n) * P(n) products, whereT(n) is the time complexity andP(n) is the number of processors used, and are for the EREW PRAM or CREW PRAM models. Our algorithms provide parallel analogues to well-known phenomena from sequential computational geometry, such as the fact that problems for polygons can oftentimes be solved more efficiently than point-set problems, and that nearest-neighbor problems can be solved without explicitly constructing a Voronoi diagram.The research of R. Cole was supported in part by NSF Grants CCR-8702271, CCR-8902221, and CCR-8906949, by ONR Grant N00014-85-K-0046, and by a John Simon Guggenheim Memorial Foundation fellowship. M. T. Goodrich's research was supported by the National Science Foundation under Grant CCR-8810568 and by the National Science Foundation and DARPA under Grant CCR-8908092.     

Ray shooting in polygons using geodesic triangulations

LetP be a simple polygon withn vertices. We present a simple decomposition scheme that partitions the interior ofP intoO(n) so-called geodesic triangles, so that any line segment interior toP crosses at most 2 logn of these triangles. This decomposition can be used to preprocessP in a very simple manner, so that any ray-shooting query can be answered in timeO(logn). The data structure requiresO(n) storage andO(n logn) preprocessing time. By using more sophisticated techniques, we can reduce the preprocessing time toO(n). We also extend our general technique to the case of ray shooting amidstk polygonal obstacles with a total ofn edges, so that a query can be answered inO( logn) time.Work by Bernard Chazelle has been supported by NSF Grant CCR-87-00917. Work by Herbert Edelsbrunner has been supported by NSF Grant CCR-89-21421. Work by Micha Sharir has been supported by ONR Grants N00014-89-J-3042 and N00014-90-J-1284, by NSF Grant CCR-89-01484, and by grants from the U.S.-Israeli Binational Science Foundation, the Fund for Basic Research administered by the Israeli Academy of Sciences, and the G.I.F., the German-Israeli Foundation for Scientific Research and Development.     

Partitioning circuits for improved testability

Exhaustive self-testing of combinational circuitry within the framework of the level-sensitive scan design (LSSD) discipline requires that every output node depend on a small number of input nodes. We present here efficient algorithms that take an arbitrary block of combinational logic and add to it the smallest number of bits of new LSSD registers necessary to: (1) partition the logic so that no output depends on more thank inputs, and (2) maintain timing within the block (so that all input-to-output paths encounter the same number of bits of register). Our partitioning algorithms conform to two different design constraints. We also show that the unconstrained partitioning problem is NP-complete.A portion of the research of the first and third authors was done while visiting Bell Communications Research. Sandeep Bhatt was also supported in part by NSF Grant DCR 84-05478 and ONR Grant N00014-82-K-0184, and Arnold Rosenberg by NSF Grants MCS-81-01213 and DMC-85-04308. A preliminary version of this paper was presented at the Fourth MIT VLSI Conference on Advanced Research in VLSI.     

An NC algorithm for recognizing tree adjoining languages

A parallel algorithm is presented for recognizing the class of languages generated by tree adjoining grammars, a tree rewriting system which has applications in natural language processing. This class of languages is known to properly include all context-free languages; for example, the noncontext-free sets {a ⁿ b ⁿ c ⁿ } and {ww} are in this class. It is shown that the recognition problem for tree adjoining languages can be solved by a concurrent read, concurrent write parallel random-access machine (CRCW PRAM) inO(logn) time using polynomially many processors. Thus, the class of tree adjoining languages is inAC ¹ and hence inNC. This extends a previous result for context-free languages.This research was supported in part by NSF Grants IRI 92-96249, MCS 82-19116-CER, MCS 82-07294, DCR 84-10413, MCS 83-05221, ARO Grant DAA29-84-9-0027, DARPA Grant N00014-85-K-0018, and by the New Jersey Institute of Technology under Grant Nos. 421690 and 211665.     

Data structures and algorithms for the string statistics problem

Given a textstringx of lengthn, theMinimal Augmented Suffix Tree T (x) ofx is a digital-search index that returns, for anyquery stringw and in a number of comparisons bounded by the length ofw, the maximum number of nonoverlapping occurrences ofw inx. It is shown that, denoting the length ofx byn, T(x) can be built in timeO(n log² n) and spaceO(n logn), off-line on a RAM.This research was supported in part, through the Leonardo Fibonacci Institute, by the Istituto Trentino di Cultura, Trento, Italy.Additional support was provided by NSF Grants CCR-8900305 and CCR-9201078, by NATO Grant CRG 900293, by the National Research Council of Italy, and by the ESPRIT III Basic Research Programme of the EC under Contract No. 9072 (Project GEPPCOM).Additional support was provided by NSF Grant CCR-91-96176 and ONR Contract N 00014-91-J-4052, ARPA Order 2225.     

Shortest paths in euclidean graphs

We analyze a simple method for finding shortest paths inEuclidean graphs (where vertices are points in a Euclidean space and edge weights are Euclidean distances between points). For many graph models, the average running time of the algorithm to find the shortest path between a specified pair of vertices in a graph withV vertices andE edges is shown to beO(V) as compared withO(E +V logV) required by the classical algorithm due to Dijkstra.Support for the first author was provided in part by NSF Grant MCS-83-08806. Support for the second author was provided in part by NSF Grants MCS-81-05324 and DCR-84-03613, an NSF Presidential Young Investigator Award, an IBM research contract, and an IBM Faculty Development Award. Support for this research was also provided in part by an ONR and DARPA under Contract N00014-83-K-0146 and ARPA Order No. 4786. Equipment support was provided by NSF Grant MCS-81-218106.     

Parallel construction of a suffix tree with applications

Many string manipulations can be performed efficiently on suffix trees. In this paper a CRCW parallel RAM algorithm is presented that constructs the suffix tree associated with a string ofn symbols inO(logn) time withn processors. The algorithm requires (n ²) space. However, the space needed can be reduced toO(n ¹⁺) for any 0< 1, with a corresponding slow-down proportional to 1/. Efficient parallel procedures are also given for some string problems that can be solved with suffix trees.The results of this paper have been achieved independently and simultaneously in [AI-86] and [LSV-86]. The research of U. Vishkin was supported by NSF Grant NSF-CCR-8615337, ONR Grant N00014-85-K-0046, and Foundation for Research in Electronics, Computers, and Communication, administered by the Israeli Academy of Sciences and Humanities. The research of A. Apostolico was carried out in part while visiting at the Istituto di Analisi dei Sistemi e Informatica, Rome, with support from the Italian National Research Council. The research of G. M. Landau, B. Schieber, and U. Vishkin was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy under Contract DE-AC02-76ER03077.     

Polynomial-time primal simplex algorithms for the minimum cost network flow problem

We present two variants of the primal network simplex algorithm which solve the minimum cost network flow problem in at mostO(n ² logn) pivots. Here we define the network simplex method as a method which proceeds from basis tree to adjacent basis tree regardless of the change in objective function value; i.e., the objective function is allowed to increase on some iterations. The first method is an extension of theminimum mean augmenting cycle-canceling method of Goldberg and Tarjan. The second method is a combination of a cost-scaling technique and a primal network simplex method for the maximum flow problem. We also show that the diameter of the primal network flow polytope is at mostn ² m.This research was supported in part by NSF Grants DMS-85-12277 and CDR-84-21402 and ONR Contract N00014-87-K0214.     

A faster parallel algorithm for a matrix searching problem

We give an improved parallel algorithm for the problem of computing the tube minima of a totally monotonen ×n ×n matrix, an important matrix searching problem that was formalized by Aggarwal and Park and has many applications. Our algorithm runs inO(log logn) time withO(n²/log logn) processors in theCRCW-PRAM model, whereas the previous best ran inO((log logn)²) time withO(n²/(log logn)² processors, also in theCRCW-PRAM model. Thus we improve the speed without any deterioration in thetime ×processors product. Our improved bound immediately translates into improvedCRCW-PRAM bounds for the numerous applications of this problem, including string editing, construction of Huffmann codes and other coding trees, and many other combinatorial and geometric problems.This research was supported by the Office of Naval Research under Grants N00014-84-K-0502 and N00014-86-K-0689, the Air Force Office of Scientific Research under Grant AFOSR-90-0107, the National Science Foundation under Grant DCR-8451393, and the National Library of Medicine under Grant R01-LM05118. Part of the research was done while the author was at Princeton University, visiting the DIMACS center.     

Topological numbering of features on a mesh

Assume we are given ann ×n binary image containing horizontally convex features; i.e., for each feature, each of its row's pixels form an interval on that row. In this paper we consider the problem of assigning topological numbers to such features, i.e., assign a number to every featuref so that all features to the left off in the image have a smaller number assigned to them. This problem arises in solutions to the stereo matching problem. We present a parallel algorithm to solve the topological numbering problem inO(n) time on ann ×n mesh of processors. The key idea of our solution is to create a tree from which the topological numbers can be obtained even though the tree does not uniquely represent the to the left of relationship of the features.The work of M. J. Atallah was supported by the Office of Naval Research under Grants N00014-84-K-0502 and N00014-86-K-0689, and the National Science Foundation under Grant DCR-8451393, with matching funds from AT&T. Part of this work was done while he was a Visiting Scientist at the Center for Advanced Architectures project of the Research Institute for Advanced Computer Science, NASA Ames Research Center, Moffett Field, CA 94035, USA. S. E. Hambrusch's work was supported by the Office of Naval Research under Contracts N00014-84-K-0502 and N00014-86K-0689, and by the National Science Foundation under Grant MIP-87-15652. Part of this work was done while she was visiting the International Computer Science Institute, Berkeley, CA 94704, USA. The work of L. E. TeWinkel was supported by the Office of Naval Research under Contract N00014-86K-0689.     

Optimal cooperative search in fractional cascaded data structures

Fractional cascading is a technique designed to allow efficient sequential search in a graph with catalogs of total sizen. The search consists of locating a key in the catalogs along a path. In this paper we show how to preprocess a variety of fractional cascaded data structures whose underlying graph is a tree so that searching can be done efficiently in parallel. The preprocessing takesO(logn) time withn/logn processors on an EREW PRAM. For a balanced binary tree, cooperative search along root-to-leaf paths can be done inO((logn)/logp) time usingp processors on a CREW PRAM. Both of these time/processor constraints are optimal. The searching in the fractional cascaded data structure can be either explicit, in which the search path is specified before the search starts, or implicit, in which the branching is determined at each node. We apply this technique to a variety of geometric problems, including point location, range search, and segment intersection search.An earlier version of this work appears inProceedings of the 2nd Annual ACM Symposium on Parallel Algorithms and Architectures, July 1990, pp. 307–316. The first author's support was provided in part by National Science Foundation Grant CCR-9007851, by the U.S. Army Research Office under Grants DAAL03-91-G-0035 and DAAH04-93-0134, and by the Office of Naval Research and the Advanced Research Projects Agency under Contract N00014-91-J-4052, ARPA Order 8225. This research was performed while the second author was at Brown University. Support was provided in part by an NSF Presidential Young Investigator Award CCR-9047466, with matching funds from IBM, by National Science Foundation Grant CCR-9007851, by the U.S. Army Research Office under Grant DAAL03-91-G-0035, and by the Office of Naval Research and the Advanced Research Projects Agency under Contract N00014-91-J-4052, ARPA Order 8225.     

Pumping lemmas for the control language hierarchy

We investigate a progression of grammatically defined language families, thecontrol language hierarchy. This hierarchy has been studied recently from the perspective of providing a linguistic framework for natural language syntax. We exhibit a progression of pumping lemmas, one for each family in the hierarchy, thereby showing that the hierarchy is strictly separable.The research reported in this paper was conducted in part at the Department of Computer and Information Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA, and was supported under ARO Grant DAA29-84-9-0027, NSF Grants MCS-8219116-CER, MCS-82-07294, DCR-84-10413 and MCS-83-05221, and DARPA Grant N00014-85-K-0018.     

A paradigm for robust geometric algorithms

This paper explores a paradigm for producing geometrical algorithms in which logical decisions that depend on finite-precision numerical calculation cannot lead to failure. It applies this paradigm to the task of intersecting two convex polyhedral objects. A key tool in this work is a method of perturbing embedding polyhedra in ways consistent with their topology.Work on this paper was supported in part by NSF Grant DMC-86-17355, NSF Grant DMS-87-02070, ONR Grant N00014-86-0281, ONR Grant N00014-88-0591, and the U.S. Army Research Office through MSI, Cornell University.     

Programming language constructs for highly parallel operations on lists

A data structure called strips is described for representing linked lists, which enables unit time access of random list elements. Running parallel prefix on strips effectively converts a list into an array. When combined with nondeterministic statement sequencing and data operations, loops for performing iterations over lists, and insertions and deletions on lists can be parallelized yielding very efficient algorithms. The strips-based representation also allows efficient serial operations on lists, which is important both when loops cannot be parallelized or when there is more parallelism than processors.This work was supported in part under ONR Grant N00014-86-K-0215 and under NSF Grant DCR-8503610.     

Optimal routing algorithms for mesh-connected processor arrays

We show that there is a randomizedoblivious algorithm for routing any (partial) permutation on ann ×n grid in 2n +O(logn) parallel communication steps. The queues will not grow larger than (logn/log logn) with high probability. We then modify this to obtain a (nonoblivious) algorithm with the same running time such that the size of the queues is bounded by a constant with high probability. For permutations withlocality, where each packet has to travel a distance at mostL, a generalization of the algorithm routes in time proportional toL with high probability. Finally, we identify a class of meshlike networks that have optimal or near-optimal diameter. These meshes have the potential of being adapted to run existing sorting and routing algorithms with corresponding reduction in their running times.Preliminary reports of portions of the results contained in this paper have appeared in theProceedings of the 1988 Aegean Workshop on Computing [5], and in theProceedings of the 1987 Conference on Foundations of Software Technology and Theoretical Computer Science [18]. The work of the first author was supported in part by NSF Grant NSF-DCR-85-03251 and ONR contract N00014-80-C-0647. The work of the second author was supported in part by NSF Grant NSF-DCR-86-00379.     

Fine-grain compilation for pipelined machines

Computer architecture design requires careful attention to the balance between the complexity of code scheduling problems and the cost and feasibility of building a machine. In this paper, we show that recently developed software pipelining algorithms produce optimal or near-optimal code for a large class of loops when the target architecture is a clean pipelined parallel machine. The important feature of these machines is the absence of structural hazards. We argue that the robustness of the scheduling algorithms and relatively simple hardware make these machines realistic and cost-effective. To illustrate the delicate balance between architecture and scheduling complexity, we show that scheduling with structural hazards is NP-hard, and that there are machines with simple structural hazards for which vectorization and the software pipelining techniques generate poor code.Supported in part by NSF Grants DCR-8502884, CCR-8704367, ONR Grant N00014-86-K-0215, and the Cornell NSF Supercomputing Center.Supported by NSF Grant CCR-8702668 and an IBM Faculty Development Award.     

AE1: An extension matrix approximate method for the general covering problem

A new approach, the extension matrix approach, is introduced and used to show that some optimization problems in general covering problem areNP-hard. Approximate solutions for these problems are given. Combining these approximate solutions, this paper presents an approximately optimal covering algorithm,AE1. Implementation shows thatAE1 is efficient and gives optimal or near optimal results.This research was supported in part by the National Science Foundation under Grant DCR 84-06801, Office of Naval Research under Grant N00014-82-K-0186, Defense Advanced Research Project Agency under Grant N00014-K-85-0878, and Education Ministry of the People's Republic of China.On leave from Harbin Institute of Technology, Harbin, China.