Serial and parallel algorithms for the medial axis transform

An O(n²) time serial algorithm is developed for obtaining the medial axis transform (MAT) of an n×n image. An O(log n) time CREW PRAM algorithm and an O(log² n) time SIMD hypercube parallel algorithm for the MAT are also developed. Both of these use O(n²) processors. Two problems associated with the MAT, the area and perimeter reporting problem, are studied. An O(log n) time hypercube algorithm is developed for both of them, where n is the number of squares in the MAT, and the algorithms use O(n²) processors     

Two-dimensional convolution on a pyramid computer

An algorithm for convolving a k×k window of weighting coefficients with an n×n image matrix on a pyramid computer of O(n²) processors in time O(logn+k²), excluding the time to load the image matrix, is presented. If k=Ω (√log n), which is typical in practice, the algorithm has a processor-time product O(n ² k²) which is optimal with respect to the usual sequential algorithm. A feature of the algorithm is that the mechanism for controlling the transmission and distribution of data in each processor is finite state, independent of the values of n and k. Thus, for convolving two {0, 1}-valued matrices using Boolean operations rather than the typical sum and product operations, the processors of the pyramid computer are finite-state     

Parallel binary search

Two arrays of numbers sorted in nondecreasing order are given: an array A of size n and an array B of size m, where n<m. It is required to determine, for every element of A, the smallest element of B (if one exists) that is larger than or equal to it. It is shown how to solve this problem on the EREW PRAM (exclusive-read exclusive-write parallel random-access machine) in O(logm logn/log log m) time using n processors. The solution is then extended to the case in which fewer than n processors are available. This yields an EREW PRAM algorithm for the problem whose cost is O(n log m, which is O(m)) for n⩽m/log m. It is shown how the solution obtained leads to an improved parallel merging algorithm     

An efficient distributed knot detection algorithm

A distributed knot detection algorithm for general graphs is presented. The knot detection algorithm uses at most O(n log n+m) messages and O(m+n log n) bits of memory to detect all knots' nodes in the network (where n is the number of nodes and m is the number of links). This is compared to O(n²) messages needed in the best algorithm previously published. The knot detection algorithm makes use of efficient cycle detection and clustering techniques. Various applications for the knot detection algorithms are presented. In particular, its importance to deadlock detection in store and forward communication networks and in transaction systems is demonstrated     

Constant time algorithms for the transitive closure and somerelated graph problems on processor arrays with reconfigurable bussystems

The transitive closure problem in O(1) time is solved by a new method that is far different from the conventional solution method. On processor arrays with reconfigurable bus systems, two O (1) time algorithms are proposed for computing the transitive closure of an undirected graph. One is designed on a three-dimensional n×n×n processor array with a reconfigurable bus system, and the other is designed on a two-dimensional n²×n² processor array with a reconfigurable bus system, where n is the number of vertices in the graph. Using the O(1) time transitive closure algorithms, many other graph problems are solved in O(1) time. These problems include recognizing bipartite graphs and finding connected components, articulation points, biconnected components, bridges, and minimum spanning trees in undirected graphs     

A novel discrete relaxation architecture

The discrete relaxation algorithm (DRA) is a computational technique that enforces arc consistency (AC) in a constraint satisfaction problem (CSP). The original sequential AC-1 algorithm suffers from O(n³m³) time complexity, and even the optimal sequential AC-4 algorithm is O (n²m²) for an n-object and m-label DRA problem. Sample problem runs show that these algorithms are all too slow to meet the need for any useful, real-time CSP applications. A parallel DRA5 algorithm that reaches a lower bound of O(nm) (where the number of processors is polynomial in the problem size) is given. A fine-grained, massively parallel hardware computer architecture has been designed for the DRA5 algorithm. For practical problems, many orders of magnitude of efficiency improvement can be reached on such a hardware architecture     

An efficient distributed protocol for finding shortest paths innetworks with negative weights

The design is discussed of distributed algorithms for the single-source shortest-path problem to run on an asynchronous directed network in which some of the edges may be associated with negative weights, and thus in which a cycle of negative total weight may also exist. The only existing solution in the literature for this problem is due to K.M. Chandy and J. Misra (1982), and it has, in the worst case, an unbounded message complexity. A synchronous version of the Chandy-Misra algorithm is described and studied, and it is proved that for a network with m edges and n nodes, the worst case message and time complexities of this algorithm are O(mn ) and O(n), respectively. This algorithm is then combined with an efficient synchronizer to yield an asynchronous protocol that retains the same message and time complexities     

Optimal parallel initialization algorithms for a class of priorityqueues

An adaptive parallel algorithm for inducing a priority queue structure on an n-element array is presented. The algorithm is extended to provide optimal parallel construction algorithms for three other heap-like structures useful in implementing double-ended priority queues, namely min-max heaps, deeps, and min-max-pair heaps. It is shown that an n-element array can be made into a heap, a deap, a min-max heap, or a min-max-pair heap in O(log n+(n /p)) time using no more than n/log n processors, in the exclusive-read-exclusive-write parallel random-access machine model     

Comments on `Parallel algorithms for hierarchical clustering andcluster validity'

In the above-titled paper (ibid., vol.12, no.11, p.1088-92, Nov. 1990), parallel implementations of hierarchical clustering algorithms that achieve O(n²) computational time complexity and thereby improve on the baseline of sequential implementations are described. The latter are stated to be O( n³), with the exception of the single-link method. The commenter points out that state-of-the-art hierarchical clustering algorithms have O(n²) time complexity and should be referred to in preference to the O(n³ ) algorithms, which were described in many texts in the 1970s. Some further references in the parallelizing of hierarchic clustering algorithms are provided     

Job scheduling in a partitionable mesh using a two-dimensionalbuddy system partitioning scheme

The job scheduling problem in a partitionable mesh-connected system in which jobs require square meshes and the system is a square mesh whose size is a power of two is discussed. A heuristic algorithm of time complexity O(n(log n+log p)), in which n is the number of jobs to be scheduled and p is the size of the system is presented. The algorithm adopts the largest-job-first scheduling policy and uses a two-dimensional buddy system as the system partitioning scheme. It is shown that, in the worst case, the algorithm produces a schedule four times longer than an optimal schedule, and, on the average, schedules generated by the algorithm are twice as long as optimal schedules     

Optimal parallel algorithms for problems modeled by a family ofintervals

A family of intervals on the real line provides a natural model for a vast number of scheduling and VLSI problems. Recently, a number of parallel algorithms to solve a variety of practical problems on such a family of intervals have been proposed in the literature. The authors develop computational tools and show how they can be used for the purpose of devising cost-optimal parallel algorithms for a number of interval-related problems, including finding a largest subset of pairwise nonoverlapping intervals, a minimum dominating subset of intervals, along with algorithms to compute the shortest path between a pair of intervals and, based on the shortest path, a parallel algorithm to find the center of the family of intervals. More precisely, with an arbitrary family of n intervals as input, all the algorithms run in O(log n) time using O(n) processors in the EREW-PRAM model of computation     

Out-of-roundness problem revisited

The properties and computation of the minimum radial separation (MRS) standard for out-of-roundness are discussed. Another standard out-of-roundness measurement called the minimum area difference (MAD) center is introduced. Although the two centers have different characteristics, the approach to finding both centers shares many commonalities. An O(n log n+k) time algorithm which is used to compute the MRS center is presented. It also computes the MAD center of a simple polygon G, where n is the number of vertices of G, and k is the number of intersection points of the medial axis and the farthest-neighbor Voronoi diagram of G. The relationship between MRS and MAD is discussed     

Optimal distributed t-resilient election in completenetworks

The problem of distributed leader election in an asynchronous complete network, in the presence of faults that occurred prior to the execution of the election algorithm, is discussed. Failures of this type are encountered, for example, during a recovery from a crash in the network. For a network with n processors, k of which start the algorithm that uses at most O(n log k +n+kt) messages is presented and shown to be optimal. An optimal algorithm for the case where the identities of the neighbors are known is also presented. It is noted that the order of the message complexity of a t-resilient algorithm is not always higher than that of a nonresilient one. The t-resilient algorithm is a systematic modification of an existing algorithm for a fault-free network     

A transaction-based approach to vertical partitioning forrelational database systems

An approach to vertical partitioning in relational databases in which the attributes of a relation are partitioned according to a set of transactions is proposed. The objective of vertical partitioning is to minimize the number of disk accesses in the system. Since transactions have more semantic meanings than attributes, this approach allows the optimization of the partitioning based on a selected set of important transactions. An optimal binary partitioning (OBP) algorithm based on the branch and bound method is presented, with the worst case complexity of O(2ⁿ), where n is the number of transactions. To handle systems with a large number of transactions, an algorithm BPi with complexity varying from O(n) to O(2ⁿ) is also developed. The experimental results reveal that the performance of vertical partitioning is sensitive to the skewness of transaction accesses. Further, BPi converges rather rapidly to OBP. Both OBP and BPi yield results comparable with that of global optimum obtained from an exhaustive search     

Optimal broadcasting on the star graph

The star graph has been show to be an attractive alternative to the widely used n-cube. Like the n-cube, the star graph possesses rich structure and symmetry as well as fault tolerant capabilities, but has a smaller diameter and degree. However, very few algorithms exists to show its potential as a multiprocessor interconnection network. Many fast and efficient parallel algorithms require broadcasting as a basic step. An optimal algorithm for one-to-all broadcasting in the star graph is proposed. The algorithm can broadcast a message to N processors in O(log₂ N) time. The algorithm exploits the rich structure of the star graph and works by recursively partitioning the original star graph into smaller star graphs. In addition, an optimal all-to-all broadcasting algorithm is developed     

An efficient heuristic for permutation packet routing on mesheswith low buffer requirements

Even though exact algorithms exist for permutation routine of n² messages on a n×n mesh of processors which require constant size queues, the constants are very large and the algorithms very complicated to implement. A novel, simple heuristic for the above problem is presented. It uses constant and very small size queues (size=2). For all the simulations run on randomly generated data, the number of routing steps that is required by the algorithm is almost equal to the maximum distance a packet has to travel. A pathological case is demonstrated where the routing takes more than the optimal, and it is proved that the upper bound on the number of required steps is O(n²). Furthermore, it is shown that the heuristic routes in optimal time inversion, transposition, and rotations, three special routing problems that appear very often in the design of parallel algorithms     

Partitioning and labeling of loops by unimodular transformations

A general method for the identification of the independent subsets in loops with constant dependence vectors is presented. It is shown that the dependence relation remains invariant under a unimodular transformation. Then a unimodular transformation is used to bring the dependence matrix into a form where the independent subsets are obtained by a direct and inexpensive partitioning algorithm. This leads to a procedure for the automatic conversion of a serial loop into a nest of parallel DO-ALL loops. Another unimodular transformation results in an algorithm to label the dependent iterations of an n-fold nested loop in O(n²) time. This provides a multithreaded dynamic scheduling scheme requiring only one fork and one join primitive     

Balanced parallel sort on hypercube multiprocessors

A parallel sorting algorithm for sorting n elements evenly distributed over 2^d p nodes of a d-dimensional hypercube is presented. The average running time of the algorithm is O((n log n)/p+p log 2n). The algorithm maintains a perfect load balance in the nodes by determining the (kn/p)th elements (k1,. . ., (p-1)) of the final sorted list in advance. These p-1 keys are used to partition the sorted sublists in each node to redistribute data to the nodes to be merged in parallel. The nodes finish the sort with an equal number of elements (n/ p) regardless of the data distribution. A parallel selection algorithm for determining the balanced partition keys in O(p log2n) time is presented. The speed of the sorting algorithm is further enhanced by the distance-d communication capability of the iPSC/2 hypercube computer and a novel conflict-free routing algorithm. Experimental results on a 16-node hypercube computer show that the sorting algorithm is competitive with the previous algorithms and faster for skewed data distributions     

A linear programming approach for the weighted graph matchingproblem

A linear programming (LP) approach is proposed for the weighted graph matching problem. A linear program is obtained by formulating the graph matching problem in L₁ norm and then transforming the resulting quadratic optimization problem to a linear one. The linear program is solved using a simplex-based algorithm. Then, approximate 0-1 integer solutions are obtained by applying the Hungarian method on the real solutions of the linear program. The complexity of the proposed algorithm is polynomial time, and it is O(n ⁶L) for matching graphs of size n. The developed algorithm is compared to two other algorithms. One is based on an eigendecomposition approach and the other on a symmetric polynomial transform. Experimental results showed that the LP approach is superior in matching graphs than both other methods