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vectorized algorithms FOR ASTRONOMICAL IMAGE-PROCESSING

PARALLEL COMPUTING 1989年第3期10卷 339-346页

作者： DEBIASE, GA CIUCCI, P COTTONE, M Istituto Astronomico Università di Roma la Sapienza Via G.M. Lancisi 29 00161 Roma Italy

Astronomical Image Processing (AIP) needs extremely interactive software systems. It is therefore necessary to execute in a short time frequently used, time consuming elaborations, such as convolution, geometrical normalization and geometrical correction. In this paper such operations have been splitted in a scalar part (typical of the operation) and in a vectorial part (in common among the whole operation set). In this way it is possible to achieve their optimization with regard to a native or attached vector processor. The common vectorial part (vectorial nucleus) is studied in detail and checked on VAX 11/780+FPS 5410, CRAY X-MP/12 and IBM 3090VF processors.

关键词： vectorized algorithms convolution image processing

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vectorized algorithms for solving special tridiagonal systems

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 1996年第12期32卷 1-14页

作者： Chung, KL Yan, WM NATL TAIWAN UNIV DEPT COMP SCI & INFORMAT ENGNTAIPEI 10764TAIWAN

Solving special tridiagonal systems often arise in the fields of engineering and science. This special tridiagonal system is diagonally dominant and circulant near-Toeplitz. This paper presents two fast vectorized algorithms for solving special tridiagonal systems. Both algorithms employ the matrix perturbation technique and have many computational advantages on vector supercomputer. The related error analysis are also given. Some experimental results are illustrated on vector uniprocessor of the GRAY X-MP EA/116se.

关键词： circulant near-Toeplitz systems CRAY X-MP diagonally dominant error analyses tridiagonal matrices vectorized algorithms

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Vectorizations of randomized matching for run-length coded strings

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PATTERN RECOGNITION LETTERS 1997年第1期18卷 63-72页

作者： Chung, KL Yan, WM NATL TAIWAN UNIV DEPT COMP SCI & INFORMAT ENGNTAIPEI 10764TAIWAN

Matching run-length coded strings (RLCSs) is very important in the field of pattern recognition. This paper considers the design of vectorized matching algorithms that operate directly on RLCSs. We first modify the algorithm of Karp and Rabin (1987) to design a linear-time randomized matching algorithm for RLCSs. Following this algorithm, two new and fast vectorized algorithms are presented. The first one is off-line and the second one is on-line. Some experiments are carried out on a GRAY X-MP EA/116se vector supercomputer to demonstrate the good performance of our vectorized algorithms. (C) 1997 Elsevier Science B.V.

关键词： pattern matching randomized algorithms vectorized algorithms run-length coded strings error probability vector supercomputer

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ROBUST PARALLEL COMPUTATION IN FLOATING-POINT AND SLI ARITHMETIC

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COMPUTING 1992年第3-4期48卷 239-257页

作者： LOZIER, DW TURNER, PR USN ACAD DEPT MATHANNAPOLISMD 21402

In this paper we consider the parallel computation of vector norms and inner products in floating-point and a proposed new form of computer arithmetic, the symmetric level-index system. The vector norms provide an illuminating example of the contrast between the two arithmetic systems under discussion in terms of the ability-to program for (complete) robustness and parallelizability. The conflict between robustness of the computation-in the sense of the dual requirements of accuracy and freedom from overflow and underflow-and easy parallelization of the algorithms within a floating-point environment is made plain. It is seen that this conflict disappears if the symmetric level-index system of arithmetic is used. The freedom from overflow and underflow offered by this system allows the programming of the straightforward definitions in a way which is simple, robust and immediately parallelizable. Numerical results are given to illustrate the fact that the symmetric level-index system yields results of comparable accuracy to those of floating-point in cases where the latter-system works and still yields results of high accuracy when the floating-point system fails altogether.

关键词： PARALLEL COMPUTATION VECTOR NORMS SYMMETRICAL LEVEL-INDEX ARITHMETIC FLOATING-POINT ARITHMETIC ERROR ANALYSIS vectorized algorithms

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A new hashing algorithm for parallel processors

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Parallel algorithms and Applications 1994年第3-4期4卷 223-237页

作者： Papadopoulos, Constantinos V. Computer Science Division National Technical University of Athens Department of Electrical and Computer Engineering Zografou GR-15773 Athens 9 Heroon Polytechneiou Greece

Hashing techniques have long been used to efficiently store and locate data indexed by key. Recently, parallel hashing algorithms have been developed that allow table insertion of many keys in a few parallel steps. The analyses of these algorithms have focused on the expected number of steps required, ignoring the-issue of work complexity. As a result, these algorithms have not been work efficient. In this paper, we present a parallel hashing algorithm that is shown to be work efficient because it performs no more work than its serial counterpart. An analysis of its behavior shows that it performs S = O(log n) expected parallel steps and W = O(n) expected work. Many parallel algorithms can make use of hashing as a core step. Procedures such as histogramming, set difference, keyed reduction and dictionary lookup can be formulated using a general hash routine. Thus, parallel hashing is an important fundamental parallel operation. The above applications may also be implemented using sorting as the core step. While the use of parallel hashing has been generally accepted in the folklore of parallel computing, a dearth of literature about the expected performance of such algorithms has led many to favor the use of sorting. This paper sheds light on the performance that may be achieved using parallel hashing algorithms and should lend credibility to their use. Performance data collected from an implementation of our algorithms on the Connection Machine CM-2 and CRAY Y-MP show that it is faster than sorting by up to a factor of four on both machines. These data show that the total time required by the algorithm is more closely related to the amount of work performed, rather than the parallel step count. Our results indicate that work efficiency is an important consideration when trying to achieve high performance on parallel machines. © 1994, Taylor & Francis Group, LLC. All rights reserved.

关键词： Data-parallel Parallel algorithms Parallel hashing vectorized algorithms

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