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     

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     

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     

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     

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     

Parallel algorithms for hierarchical clustering and clustervalidity

Parallel algorithms on SIMD (single-instruction stream multiple-data stream) machines for hierarchical clustering and cluster validity computation are proposed. The machine model uses a parallel memory system and an alignment network to facilitate parallel access to both pattern matrix and proximity matrix. For a problem with N patterns, the number of memory accesses is reduced from O(N ³) on a sequential machine to O(N²) on an SIMD machine with N PEs     

Designing efficient parallel algorithms on mech-connected computerswith multiple broadcasting

Semigroup and prefix computations on two-dimensional mesh-connected computers with multiple broadcasting (2-MCCMBs) are studied. Previously, only square 2-MCCMBs with N processing elements were considered for semigroup computations of N data items, and O(N^1/6) time was required. It is found that square machines are not the best form for semigroup computations, and an O(N^1/8)-time algorithm is derived on an N^5/8×N^3/8 rectangular 2-MCCMB. This time complexity can be further reduced to O(N^1/9) if fewer processing elements are used. Parallel algorithms for prefix computations with the same time complexities are derived     

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     

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     

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     

Computing the width of a set

For a set of points P in three-dimensional space, the width of P, W (P), is defined as the minimum distance between parallel planes of support of P. It is shown that W(P) can be computed in O(n log n +I) time and O(n) space, where I is the number of antipodal pairs of edges of the convex hull of P, and n is the number of vertices; in the worst case, I=O( n²). For a convex polyhedra the time complexity becomes O(n+I). If P is a set of points in the plane, the complexity can be reduced to O(nlog n). For simple polygons, linear time suffices     

A processor-time-minimal systolic array for transitive closure

Using a directed acyclic graph (DAG) model of algorithms, the authors focus on processor-time-minimal multiprocessor schedules: time-minimal multiprocessor schedules that use as few processors as possible. The Kung, Lo, and Lewis (KLL) algorithm for computing the transitive closure of a relation over a set of n elements requires at least 5n-4 parallel steps. As originally reported. their systolic array comprises n² processing elements. It is shown that any time-minimal multiprocessor schedule of the KLL algorithm's dag needs at least n²/3 processing elements. Then a processor-time-minimal systolic array realizing the KLL dag is constructed. Its processing elements are organized as a cylindrically connected 2-D mesh, when n=0 mod 3. When n≠0 mod 3, the 2-D mesh is connected as a torus     

A processor-time-minimal systolic array for cubical mesh algorithms

Using a directed acyclic graph (DAG) model of algorithms, the paper focuses on time-minimal multiprocessor schedules that use as few processors as possible. Such a processor-time-minimal scheduling of an algorithm's DAG first is illustrated using a triangular shaped 2-D directed mesh (representing, for example, an algorithm for solving a triangular system of linear equations). Then, algorithms represented by an n×n×n directed mesh are investigated. This cubical directed mesh is fundamental; it represents the standard algorithm for computing matrix product as well as many other algorithms. Completion of the cubical mesh required 3n-2 steps. It is shown that the number of processing elements needed to achieve this time bound is at least [3n^2/4]. A systolic array for the cubical directed mesh is then presented. It completes the mesh using the minimum number of steps and exactly [3n ^2/4] processing elements it is processor-time-minimal. The systolic array's topology is that of a hexagonally shaped, cylindrically connected, 2-D directed mesh     

Convolution on mesh connected multicomputers

An efficient parallel algorithm is presented for convolution on a mesh-connected computer with wraparound. The algorithm does not require a broadcast feature for data values, as assumed by previously proposed algorithms. As a result, the algorithm is applicable to both SIMD and MIMD meshes. For an N×N image and a M×M template, the previous algorithms take O (M²q) time on an N×N mesh-connected multicomputer (q is the number of bits in each entry of the convolution matrix). The algorithms have complexity O(M²r), where r=max {number of bits in an image entry, number of bits in a template entry}. In addition to not requiring a broadcast capability, these algorithms are faster for binary images     

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     

Fast image labeling using local operators on mesh-connectedcomputers

A new parallel algorithm is proposed for fat image labeling using local operators on image pixels. The algorithm can be implemented on an n×n mesh-connected computer such that, for any integer k in the range [1, log (2n)], the algorithm requires Θ(kn¹k/) bits of local memory per processor and takes Θ(kn) time. Bit-serial processors and communication links can be used without affecting the asymptotic time complexity of the algorithm. The time complexity of the algorithm has very small leading constant factors, which makes it superior to previous mesh computer labeling algorithms for most practical image sizes (e.g. up to 4096×4096 images). Furthermore, the algorithm is based on using stacks that can be realized using very fast shift registers within each processing element     

Efficient algorithms for list ranking and for solving graphproblems on the hypercube

A hypercube algorithm to solve the list ranking problem is presented. Let n be the length of the list, and let p be the number of processors of the hypercube. The algorithm described runs in time O(n/p) when n=Ω(p ^1+ε) for any constant ε>0, and in time O(n log n/p+log³ p) otherwise. This clearly attains a linear speedup when n=Ω(p ^1+ε). Efficient balancing and routing schemes had to be used to achieve the linear speedup. The authors use these techniques to obtain efficient hypercube algorithms for many basic graph problems such as tree expression evaluation, connected and biconnected components, ear decomposition, and st-numbering. These problems are also addressed in the restricted model of one-port communication     

An efficient digital search algorithm by using a double-arraystructure

An efficient digital search algorithm that is based on an internal array structure called a double array, which combines the fast access of a matrix form with the compactness of a list form, is presented. Each arc of a digital search tree, called a DS-tree, can be computed from the double array in 0(1) time; that is to say, the worst-case time complexity for retrieving a key becomes 0(k) for the length k of that key. The double array is modified to make the size compact while maintaining fast access, and algorithms for retrieval, insertion, and deletion are presented. If the size of the double array is n+cm, where n is the number of nodes of the DS-tree, m is the number of input symbols, and c is a constant particular to each double array, then it is theoretically proved that the worst-case times of deletion and insertion are proportional to cm and cm², respectively, and are independent of n. Experimental results of building the double array incrementally for various sets of keys show that c has an extremely small value, ranging from 0.17 to 1.13     

Memory and processing architecture for 3D voxel-based imagery

A versatile voxel-based architecture for 3-D volume visualization, called the Cube architecture, is introduced. A small-scale prototype of the architecture has been realized in hardware and has been operating in true real-time, faster than the alternative voxel systems. The Cube architecture is centered around a 3-D cubic frame buffer, of voxels, and it entertains three processors that access the frame buffer to input sampled and synthetic data, to manipulate the 3-D images, and to project and render them. To cope with the huge quantity of voxels and still perform in real-time, two special features were incorporated within the architecture: a unique skewed memory organization, which permits the retrieval and storage of voxels in parallel, and a multiple-write bus, which speeds up the viewing process. These features allow Cube, for example, to project an image of n³ voxels in O(n ² log n) time rather than the conventional O( n³) time     

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