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The paper reviews existing methods for generating discrete random variables and their suitability for vector processing. A new method for generating discrete random variables for use in vectorized Monte Carlo simulations is presented. The method uses the concept of importance sampling and generates random variables by employing uniform distribution to speed up the computation. The sampled random variables are subsequently adjusted so that unbiased estimates are obtained. The method preserves both the mean and variance of the original distribution. It is demonstrated that the method requires simpler coding and shorter execution time for both scalar and vector processing, when compared with other existing methods. The vectorization speedup of the method is demonstrated on an IBM 3090–180 machine with a vector facility.

The i860™ is a high performance microprocessor used in the Intel Touchstone project. This paper proposes a paradigm for programming the i860™ that is modelled on the vector instructions of the Cray computers. A collection of Fortran callable assembler subroutines (Naspack routines) were written that mimic the concurrent vector instructions of the Cray with cache taking the place of vector registers. Using the Naspack subroutines on an adisolve, we obtained a speedup of 3.9 for a 128 by 128 system over compiled code.

Many direct methods of solution are available for solving nonlinear Volterra integral and integro-differential equations. All of these methods are inherently serial and therefore have not received much attention for use on a vector or parallel computer. It is possible, however, to make modest gains in speedup by employing some novel approaches to existing methods. These modifications are discussed and numerical examples illustrate the results.

The performance of various vectorizable discrete random-sampling methods, along with the commonly used inverse sampling method, is assessed on a vector machine. Monte Carlo applications involving, one-dimensional, two-dimensional and multi-dimensional probability tables are used in the investigation. Various forms of the weighted sampling method and methods that transform the original probability table are examined. It is found that some form of weighted sampling is efficient, when the original probability distribution is not far from uniform or can be approximated analytically. Table transformation methods, though requiring additional memory storage, are best suited in applications where multidimensional tables are involved.

In this paper a novel Fuzzy Rule Based Dissimilarity Function is presented, to determine the hierarchical merging sequence in a region based segmentation scheme. The proposed technique, based on distinct region features and fuzzy logic principles, is designed to cope with the problems inherent in the segmentation task that the traditional merging cost functions cannot overcome. It combines the global (color) and local (spatial) information of the image to compare two adjacent regions in the rgb space. The validity of the approach has been subjectively and objectively verified for several types of color images such as head and shoulders, natural and texture images.