(1) time Parallel Agorithm for Finding 2D Convex Hull on a Reconfigurable Mesh Computer Architecture

In this paper we propose a parallel algorithm in image processing in (1) time, intended for a parallel machine '' Reconfigurable Mesh Computer (RMC), of size n x n Elementary Processors (PE). The algorithm consists in determining the convex envelope of a two-level 2D image with a complexity in (1) time. The approach used is purely geometric. It is based solely on the projection of the coordinates of PEs retained in specific quadrants and on the application of the algorithm that determines the Min / Max in (1) time. This has reduced the complexity of the algorithm for determining the convex hull at (1) time

Fast Inner Product Computation on Short Buses

We propose a VLSI inner product processor architecture involving broadcasting only over short buses (containing less than 64 switches). The architecture leads to an efficient algorithm for the inner product computation. Specifically, it takes 13 broadcasts, each over less than 64 switches, plus 2 carry-save additions (tcsa) and 2 carry-lookahead additions (tcla) to compute the inner product of two arrays of N = 29 elements, each consisting of m = 64 bits. Using the same order of VLSI area, our algorithm runs faster than the best known fast inner product algorithm of Smith and Torng [ Design of a fast inner product processor, Proceedings of IEEE 7th Symposium on Computer Arithmetic (1985)], which takes about 28 tcsa + tcla for the computation

A fast adaptive convex hull algorithm on two-dimensional processor arrays with a reconfigurable BUS system

A bus system that can change dynamically to suit computational needs is referred to as reconfigurable. We present a fast adaptive convex hull algorithm on a two-dimensional processor array with a reconfigurable bus system (2-D PARBS, for short). Specifically, we show that computing the convex hull of a planar set of n points taken O(log n/log m) time on a 2-D PARBS of size mn x n with 3 less than or equal to m less than or equal to n. Our result implies that the convex hull of n points in the plane can be computed in O(1) time in a 2-D PARBS of size n(exp 1.5) x n

Parallel Algorithm for Brain Tissues Segmentation in T1-Weighted MR Images on 3D Reconfigurable Mesh Computer

In this paper, we propose a parallel algorithm for brain tissues segmentation from T1-weighted Magnetic Resonance Images (MRI) on Massively Parallel architecture named reconfigurable mesh computer (MCR), this brain tissues are already extracted using our method named Threshold Morphologic Brain Extraction method (TMBE)[1]. The use of this massively parallel architecture is introduced in order to improve the complexities of the corresponding algorithms. The image of size (M x N x K) to be processed must be stored on the RMC of the same size, one Voxel per Processing Element (PE). The proposed method consists in the brain tissues segmentation using parallel version of the modified fuzzy c-means MFCM [2], named PMFCM. This algorithm is directly applied on the extracted volume. The corresponding parallel program of the proposed algorithm is validated on a 3D Reconfigurable Mesh emulator [3]

Optimal computation of the contour of maximal elements on constrained reconfigurable meshes

The Reconfigurable Mesh (RM) attracted criticism for its key assumption that a message can be broadcast in constant time independent of bus length To account for this limit Beresford-Smith et al. have recently proposed k-constrained RM where buses of length at most k, a constant, are allowed to b formed. Straightforward simulations of optimal RM algorithms on this constrained RM model are found to be non-optimal. This paper presents two optimal algorithms to compute the contour of maximal elements of a set of planar points

The distributed ASCI supercomputer project

The Distributed ASCI Supercomputer (DAS) is a homogeneous wide-area distributed system consisting of four cluster computers at different locations. DAS has been used for research on communication software, parallel languages and programming systems, schedulers, parallel applications, and distributed applications. The paper gives a preview of the most interesting research results obtained so far in the DAS project

Adaptive AT2 Optimal Algorithms on reconfigurable meshes

Recently a few self-simulation algorithms have been developed to execute algorithms on a reconfigurable mesh (RM) of size smaller than recommended in those algorithms. Optimal slowdown, in self-simulation, has been achieved with the compromise that the resultant algorithms fail to remain AT2 optimal. In this paper we have introduced, for the first time, the idea of adaptive algorithm which runs on RM of variable sizes without compromising the AT2 optimality. We have supported our idea by developing adaptive algorithms, for sorting items and computing the contour of maximal elements of a set of planar points on RM. We have also conjectured that to obtain an AT2 algorithm to solve a problem of size n with I(n) information content on an RM of size p x q where pq=kI(n), it is sufficient to form buses of length O (k)

An integrated associative processing system

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (p. 97-105).by Frederick Paul Herrmann.Ph.D