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Describes intuitively the fact that four types of formal languages can be generated by four types of grammars or can be recognized by four types of automata. Gives the relationships between context‐sensitive languages and computer programming languages. Defines and investigates parallel productions, parallel grammars, and context‐free parallel grammars. Shows that context‐sensitive languages exist which can be generated by context‐free parallel grammars. In addition, states the advantages of context‐free parallel grammars. Also shows that context‐free languages (CFL) are a proper subset of context‐free parallel languages (CFPL). Furthermore, CFPL is a more effective tool for modelling computer programming languages than CFL, especially for parallel computer programming languages, for example, the ADA programming language. Also illustrates context‐sensitive property of recognizing hand‐written characters. The results may have useful applications in artificial intelligence, model parallel computer programming languages, software engineering, expert systems and robotics.

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

The purpose of this paper is to measure the financial risk and optimal capital structure of a corporation.

Irregular disjunctive programming problems arising in firm models and risk management can be solved by the techniques presented in the paper.

Parallel processing and mathematical modeling provide a fruitful basis for solving ultra-scale non-convex general disjunctive programming (GDP) problems, where the computational challenge in direct mixed-integer non-linear programming (MINLP) formulations or single processor algorithms would be insurmountable.

The test is limited to a single firm in an experimental setting. Repeating the test on large sample of firms in future research will indicate the general validity of Monte-Carlo-based VAR estimation.

The authors show that the risk surface of the firm can be approximated by integrated use of accounting logic, corporate finance, mathematical programming, stochastic simulation and parallel processing.

Parallel processing has potential to simplify large-scale MINLP and GDP problems with non-convex, multi-modal and discontinuous parameter generating functions and to solve them faster and more reliably than conventional approaches on single processors.

The purpose of this paper is to present a parallel implementation of an evolution strategy (ES) algorithm for optimization of electromagnetic devices. It is intended for multi‐core processors and for optimization problems that have objective function representing a numerical simulation of electromagnetic devices. The speed‐up of the optimization is evaluated as a function of the number of processor cores used.

Two parallelization approaches are implemented in the program developed – using multithreaded programming and using OpenMP. Their advantages and drawbacks are discussed. The program is tested on two examples for optimization of electromagnetic devices.

Using the developed parallel ES algorithm on a quad‐core processor, the optimization time can be reduced 2.4‐3 times, instead of the expected four times. This is due to a number of system processes and programs that run on part of the cores.

A new parallel ES optimization algorithm has been developed and investigated. The paper could be useful for researchers aiming to diminish the optimization time by using parallel evolution optimization on multi‐core processors.

In recent years, there is a gradual shift from sequential computing to parallel computing. Nowadays, nearly all computers are of multicore processors. To exploit the available cores, parallel computing becomes necessary. It increases speed by processing huge amount of data in real time. The purpose of this paper is to parallelize a set of well-known programs using different techniques to determine best way to parallelize a program experimented.

A set of numeric algorithms are parallelized using hand parallelization using OpenMP and auto parallelization using Pluto tool.

The work discovers that few of the algorithms are well suited in auto parallelization using Pluto tool but many of the algorithms execute more efficiently using OpenMP hand parallelization.

The work provides an original work on parallelization using OpenMP programming paradigm and Pluto tool.

Some issues related to effective parallel implementation of the combined finite‐discrete element approach on PC clusters are discussed. Attention is focused on the interprocessor communications. Three communication schemes suitable for different problems are presented. The worker‐to‐manager scheme is simple to implement. The neighbour‐to‐neighbour scheme is sophisticated with regard to programming, and requires extra memory space, but has good overall performance for larger problems. The mixed worker‐manager scheme can balance the difficulty in programming and the overall communication performance. The effects of subdomain buffer zone on communications are also demonstrated by numerical examples.

Presents a review on implementing finite element methods on supercomputers, workstations and PCs and gives main trends in hardware and software developments. An appendix included at the end of the paper presents a bibliography on the subjects retrospectively to 1985 and approximately 1,100 references are listed.

Presents a new powerful multiple‐criteria decision‐making (MCDM) framework with an approach that combines recent results from several related areas. The De Novo programming and external reconstruction approach (ERA) provides the overall structure for the algorithms and the sequence of r‐constrained linear programs generated by the ERA‐framework is solved. This is by a parallel implementation of a powerful interior point algorithm called the conjugate gradient method (CGM), selected because it is particularly suitable for parallel processing. Gives details of the proposed parallel implementation of the CGM, together with the algorithm. Presents an analysis of the parallel performance and discusses theoretical speed‐up.