Our aim in this book is to present and enlarge upon those aspects of parallel computing that are needed by practitioners of computational science. Today al most all classical sciences, such as mathematics, physics, chemistry and biology, employ numerical methods to help gain insight into nature. In addition to the traditional numerical methods, such as matrix inversions and the like, a whole new field of computational techniques has come to assume central importance, namely the numerical simulation methods. These methods are much less fully developed than those which are usually taught in a standard numerical math ematics course. However, they form a whole new set of tools for research in the physical sciences and are applicable to a very wide range of problems. At the same time there have been not only enormous strides forward in the speed and capability of computers but also dramatic new developments in computer architecture, and particularly in parallel computers. These improvements offer exciting prospects for computer studies of physical systems, and it is the new techniques and methods connected with such computer simulations that we seek to present in this book, particularly in the light of the possibilities opened up by parallel computers. It is clearly not possible at this early stage to write a definitive book on simulation methods and parallel computing.
The authors present techniques and algorithms in computational science that have been developed for parallel computers. The numerical methods that have evolved in recent years to investigate problems in physics, mathematics, chemistry and biology by means of computer simulations are now being extended to make use of the opportunities opened up by the development of parallel computers. To utilize this potentially vast increase in computer power, it is necessary to devise algorithms that are appropriate to the particular problem being investigated. This volume combines a detailed consideration of the computer architecture (hardware and software feature as well as inter-processor communication schemes) with an understanding of the physical system being studied. These two aspects together make it possible to develop techniques that make optimum use of the available parallelism. Attention is focused on the Monte Carlo and molecular dynamics methods, and the techniques and algorithms are illustrated with examples from the statistical mechanics of spin systems, polymers and other many-particle systems.
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