Parallel Computation: Models And Methods

Transformational programming and parallel computation are two emerging fields that may ultimately depend on each other for success. Perhaps because ad hoc programming on sequential machines is so straightforward, sequential programming methodology has had little impact outside the academic community, and transformational methodology has had little impact at all. However, because ad hoc programming for parallel machines is so hard, and because progress in software construction has lagged behind architectural advances for such machines, there is a much greater need to develop parallel programming and transformational methodologies. Parallel Algorithm Derivation and Program Transformation stimulates the investigation of formal ways to overcome problems of parallel computation, with respect to both software development and algorithm design. It represents perspectives from two different communities: transformational programming and parallel algorithm design, to discuss programming, transformational, and compiler methodologies for parallel architectures, and algorithmic paradigms, techniques, and tools for parallel machine models. Parallel Algorithm Derivation and Program Transformation is an excellent reference for graduate students and researchers in parallel programming and transformational methodology. Each chapter contains a few initial sections in the style of a first-year, graduate textbook with many illustrative examples. The book may also be used as the text for a graduate seminar course or as a reference book for courses in software engineering, parallel programming or formal methods in program development.

The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kinds of genetic modeling.

Computing has moved away from a focus on performance-centric serial computation, instead towards energy-efficient parallel computation. This provides continued performance increases without increasing clock frequencies, and overcomes the thermal and power limitations of the dark-silicon era. As the number of parallel cores increases, we transition into the many-core computing era.

Computing has moved away from a focus on performance-centric serial computation, instead towards energy-efficient parallel computation. This provides continued performance increases without increasing clock frequencies, and overcomes the thermal and power limitations of the dark-silicon era. As the number of parallel cores increases, we transition into the many-core computing era.

This volume provides insight into modelling and ultimate limit computation of complex structures, with their components represented by solid deformable bodies. The book examines practically all the important questions of current interests for nonlinear solid mechanics: plasticity, damage, large deformations, contact, dynamics, instability, localisation and failure, discrete models, multi-scale, multi-physics and parallel computing, with special attention given to finite element solution methods.