An Introduction to High-performance Scientific ComputingHardback Scientific & Engineering Computation
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- Publisher: MIT Press
- Format: Hardback | 750 pages
- Dimensions: 212mm x 235mm x 49mm | 1,971g
- Publication date: 3 June 1996
- Publication City/Country: Cambridge, Mass.
- ISBN 10: 0262061813
- ISBN 13: 9780262061810
- Edition statement: New.
- Illustrations note: Illustrations (some col.)
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.Designed for undergraduates, An Introduction to High-Performance Scientific Computing assumes a basic knowledge of numerical computation and proficiency in Fortran or C programming and can be used in any science, computer science, applied mathematics, or engineering department or by practicing scientists and engineers, especially those associated with one of the national laboratories or supercomputer centers.The authors begin with a survey of scientific computing and then provide a review of background (numerical analysis, IEEE arithmetic, Unix, Fortran) and tools (elements of MATLAB, IDL, AVS). Next, full coverage is given to scientific visualization and to the architectures (scientific workstations and vector and parallel supercomputers) and performance evaluation needed to solve large-scale problems. The concluding section on applications includes three problems (molecular dynamics, advection, and computerized tomography) that illustrate the challenge of solving problems on a variety of computer architectures as well as the suitability of a particular architecture to solving a particular problem.Finally, since this can only be a hands-on course with extensive programming and experimentation with a variety of architectures and programming paradigms, the authors have provided a laboratory manual and supporting software via anonymous ftp.Scientific and Engineering Computation series
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Ashton B. Carter is Ford Foundation Professor of Science and International Affairs at Harvard University's John F. Kennedy School of Government and Co-director of the Preventive Defense Project.
Table of contents
An overview of scientific computing: introduction, large-scale scientific problems, the scientific computing environment, workstations, supercomputers, further reading. Part 1 Background: a review of selected topics from numerical analysis - notation, error, floating-point numbers, Taylor's series, linear algebra, differential equations, fourier series; IEEE arithmetic short reference - single precision, double precision, rounding, infinity, NaN, and zero, of things not said, further reading; UNIX, vi, and ftp - a quick review - UNIX short reference, vi short reference, ftp short reference; elements of UNIX make - introduction, an example of using make, some advantages of make, the makefile, further examples, dynamic macros, user-defined macros, additional features, other examples, a makefile for C, creating your own makefile, futher information, a makefile for fortran modules, a makefile for C modules; elements of fortran - introduction, overview, definitions and basic rules, description of statements, reading and writing, examples. Part 2 Tools: elements of matlab - what is MATLAB?, getting started, some examples, short outline of the language, built-in functions, MATLAB scripts and user-defined functions, input/output, graphics, that's it!; elements of IDL - getting started, exploring the basic concepts, plotting, programming in IDL, input/output, using IDL efficiently, summary; elements of AVS - basic concepts, AVS graphical programming - the Network editor, the geometry viewer, AVS applications, further reading. Part 3 Scientific visualization: scientific visualization - definitions and goals of scientific visualization, history of scientific visualization, example of scientific visualization, concepts of scientific visualization, visual cues, characterization of scientific data, visualization techniques, annotations, interactivity, interpretation goals to pursue with visualization, quantitative versus qualitative data interpretation. Part 4 Architectures: computer performance - introduction and background, computer performance, benchmarks, the effect of optimizing compilers, other architectural factors, vector and parallel computers, summary. (Part contents).