Himabindu Gurla

Square meshes are not optimal for convex hull computation

Recently it has been noticed that for semigroup computations and for selection, rectangular meshes with multiple broadcasting yield faster algorithms than their square counterparts. The contribution of this paper is to provide yet another example of a fundamental problem for which this phenomenon occurs.

Leftmost one computation on meshes with row broadcasting

Abstract Given a matrix of size √ n ×√ n with every entry being a 0 or a 1, the leftmost-one problem asks to determine the position of the leftmost 1, if any, in each row of the matrix. The leftmost-one problem finds applications in image processing, digitized geometry and computer graphics, among others. Recently, an O( n 1 6 ) time solution to the leftmost-one problem on a mesh with row buses has been proposed. However, the computational model assumes that processors have unbounded memory. We show that the problem can be solved in O( n 1 6 ) time on a √ n ×√ n mesh with row broadcasting, even if each processor has only a constant number of registers.

Constant-time convexity problems on reconfigurable meshes

Abstract The purpose of this paper is to demonstrate that the versatility of the reconfigurable mesh can be exploited to devise constant-time algorithms for a number of important computational tasks relevant to robotics, computer graphics, image processing, and computer vision. In all our algorithms, we assume that one or two n -vertex (convex) polygons are pretiled, one vertex per processor, onto a reconfigurable mesh of size √ n × √ n . In this setup, we propose constant-time solutions for testing an arbitrary polygon for convexity, solving the point location problem, solving the supporting lines problem, solving the stabbing problem, determining the minimum area/perimeter corner triangle for a convex polygon, determining the k -maximal vertices of a restricted class of convex polygons, constructing the common tangents of two separable convex polygons, deciding whether two convex polygons intersect, answering queries concerning two convex polygons, and computing the smallest distance between the boundaries of two convex polygons. To the best of our knowledge, this is the first time that O (1) time algorithms to solve dense instances of these problems are proposed on reconfigurable meshes.

Time-optimal domain-specific querying on enhanced meshes

Query processing is a crucial component of various application domains including information retrieval, database design and management, pattern recognition, robotics, and VLSI. Many of these applications involve data stored in a matrix satisfying a number of properties. One property that occurs time and again specifies that the rows and the columns of the matrix are independently sorted. It is customary to refer to such a matrix as sorted. An instance of the batched searching and ranking problem (BSR) involves a sorted matrix A of items from a totally ordered universe, along with a collection Q of queries. Q is an arbitrary mix of the following query types: for a search query q/sub j/, one is interested in an item of A that is closest to q/sub j/; for a rank query q/sub j/ one is interested in the number of items of A that are strictly smaller than q/sub j/. The BSR problem asks for solving all queries in Q. The authors consider the BSR problem in the following context: the matrix A is pretiled, one item per processor, onto an enhanced mesh of size /spl radic/n/spl times//spl radic/n; the m queries are stored, one per processor, in the first m//spl radic/n~ columns of the platform. Their main contribution is twofold. First, they show that any algorithm that solves the BSR problem must take at least /spl Omega/(max{logn, /spl radic/m}) time in the worst case. Second, they show that this time lower bound is tight on meshes of size /spl radic/n/spl times//spl radic/n enhanced with multiple broadcasting, by exhibiting an algorithm solving the BSR problem in /spl Theta/(max{logn, /spl radic/m}) time on such a platform.

Time- and VLSI-optimal Sorting on Meshes with Multiple Broadcasting

In this work, we present a time-and VLSI-optimal sorting algorithm for meshes with multiple broadcasting. Specifically, we show that for every choice of a positive integer constant c, m items \left( {n^{\frac{1} {2} + \frac{1} {{2c}}} \leqslant m \leqslant n} \right) stored in the first \left\lceil {\frac{m} {{\sqrt n }}} \right\rceil columns of a mesh with multiple broadcasting of size \sqrt {n} x \sqrt {n} can be sorted in O({\frac{m} {{\sqrt n }}}) time.

Time- and VLSI-optimal sorting on enhanced meshes

Sorting is a fundamental problem with applications in all areas of computer science and engineering. In this work, we address the problem of sorting on mesh connected computers enhanced by endowing each row and each column with its own dedicated high-speed bus. This architecture, commonly referred to as a mesh with multiple broadcasting, is commercially available and has been adopted by the DAP family of multiprocessors. Somewhat surprisingly, the problem of sorting m, (m/spl les/n), elements on a mesh with multiple broadcasting of size /spl radic/n/spl times//spl radic/n has been studied, thus far, only in the sparse case, where m/spl isin//spl Theta/(/spl radic/n) and in the dense case, where m/spl isin//spl Theta/O(/spl radic/n). Yet, many applications require using an existing platform of size /spl radic/n/spl times//spl radic/n for sorting m elements, with /spl radic/n<m/spl les/n. Our main contribution is to present the first known adaptive time- and VLSI-optimal sorting algorithm for meshes with multiple broadcasting. Specifically we show that, for every choice of a constant 0</spl epsiv//spl les/ 1/2 , it is possible to sort m elements, n/sup 1/2 +/spl epsiv///spl les/m/spl les/n, stored in the leftmost [m//spl radic/n] columns of a mesh with multiple broadcasting of size /spl radic/n/spl times//spl radic/n in /spl Theta/(m//spl radic/n) time.

Podality-based time-optimal computations on enhanced meshes

The main contribution of this paper is to present simple and elegant podality-based algorithms for a variety of computational tasks motivated by, and finding applications to, pattern recognition, computer graphics, computational morphology, image processing, robotics, computer vision, and VLSI design. The problems that we address involve computing the convex hull, the diameter, the width, and the smallest area enclosing rectangle of a set of points in the plane, as well as the problems of finding the maximum Euclidian distance between two planar sets of points, and of constructing the Minkowski sum of two convex polygons. Specifically, we show that once we fix a positive constant /spl epsiv/, all instances of size m, (n/sup 1/2 +/spl epsiv///spl les/m/spl les/n) of the problems above, stored in the first [m//spl radic/n] columns of a mesh with multiple broadcasting of size /spl radic/n/spl times//spl radic/n can be solved time-optimally in /spl Theta/(m//spl radic/n) time.

Constant-time algorithms for constrained triangulations on reconfigurable meshes

A number of applications in computer-aided manufacturing, CAD, and computer-aided geometric design ask for triangulating pieces of material with defects. These tasks are known collectively as constrained triangulations. Recently, a powerful architecture called the reconfigurable mesh has been proposed: In essence, a reconfigurable mesh consists of a mesh-connected architecture augmented by a dynamically reconfigurable bus system. The main contribution of this paper is to show that the flexibility of the reconfigurable mesh can be exploited for the purpose of obtaining constant-time algorithms for a number of constrained triangulation problems. These include triangulating a convex planar region containing any constant number of convex holes, triangulating a convex planar region in the presence of a collection of rectangular holes, and triangulating a set of ordered line segments. Specifically with a collection of O(n) such objects as input, our algorithms run in O(1) time on a reconfigurable mesh of size n/spl times/n. To the best of our knowledge, this is the first time constant time solutions to constrained triangulations are reported on this architecture.

TIME-OPTIMAL DIGITAL GEOMETRY ALGORITHMS ON MESHES WITH MULTIPLE BROADCASTING

The main contribution of this work is to show that a number of digital geometry problems can be solved elegantly on meshes with multiple broadcasting by using a time-optimal solution to the leftmost one problem as a basic subroutine. Consider a binary image pretiled onto a mesh with multiple broadcasting of size one pixel per processor. Our first contribution is to prove an Ω(n1/6) time lower bound for the problem of deciding whether the image contains at least one black pixel. We then obtain time lower bounds for many other digital geometry problems by reducing this fundamental problem to all the other problems of interest. Specifically, the problems that we address are: detecting whether an image contains at least one black pixel, computing the convex hull of the image, computing the diameter of an image, deciding whether a set of digital points is a digital line, computing the minimum distance between two images, deciding whether two images are linearly separable, computing the perimeter, area and width of a given image. Our second contribution is to show that the time lower bounds obtained are tight by exhibiting simple O(n1/6) time algorithms for these problems. As previously mentioned, an interesting feature of these algorithms is that they use, directly or indirectly, an algorithm for the leftmost one problem recently developed by one of the authors.

Time- and VLSI-optimal convex hull computation on meshes with multiple broadcasting

Our main contribution is to present the first known general-case, time- and VLSI-optimal, algorithm for convex hull computation on meshes with multiple broadcasting. Specifically, we show that for every choice of a positive constant e, the convex hull of a set of an arbitrary set of m (n12 + e ⩽ m ⩽ n) points in the plane input in the first ⌉m√n⌈ columns of a mesh with multiple broadcasting of size √n × √n can be computed in Θ (m√n) time.