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Scientific & Engineering Computation

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Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time- and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field.

A Guide to the Implementation and Application of PC Clusters

Supercomputing research--the goal of which is to make computers that are ever faster and more powerful--has been at the cutting edge of computer technology since the early 1960s. Until recently, research cost in the millions of dollars, and many of the companies that originally made supercomputers are now out of business.The early supercomputers used distributed computing and parallel processing to link processors together in a single machine, often called a mainframe.

ZPL is a new array programming language for science and engineering computation. Designed for fast execution on both sequential and parallel computers, it is intended to replace languages such as Fortran and C. Because ZPL benefits from recent research in parallel compilation, it provides a convenient high-level programming medium for supercomputers with efficiency comparable to hand-coded message-passing programs.

Analysis and Design

The concept of fuzzy sets is one of the most fundamental and influential tools in computational intelligence. Fuzzy sets can provide solutions to a broad range of problems of control, pattern classification, reasoning, planning, and computer vision. This book bridges the gap that has developed between theory and practice. The authors explain what fuzzy sets are, why they work, when they should be used (and when they shouldn't), and how to design systems using them.

A Modeling Language for Global Optimization

PLAPACK is a library infrastructure for the parallel implementation of linear algebra algorithms and applications on distributed memory supercomputers such as the Intel Paragon, IBM SP2, Cray T3D/T3E, SGI PowerChallenge, and Convex Exemplar. This infrastructure allows library developers, scientists, and engineers to exploit a natural approach to encoding so-called blocked algorithms, which achieve high performance by operating on submatrices and subvectors.

From a Programming Perspective

This text evolved from a new curriculum in scientific computing that was developed to teach undergraduate science and engineering majors how to use high-performance computing systems (supercomputers) in scientific and engineering applications.

A Users' Guide and Tutorial for Network Parallel Computing

Written by the team that developed the software, this tutorial is the definitive resource for scientists, engineers, and other computer users who want to use PVM to increase the flexibility and power of their high-performance computing resources. PVM introduces distributed computing, discusses where and how to get the PVM software, provides an overview of PVM and a tutorial on setting up and running existing programs, and introduces basic programming techniques including putting PVM in existing code.

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