A clear illustration of how parallel computers can be successfully applied to large-scale scientific computations. This book demonstrates how a variety of applications in physics, biology, mathematics and other sciences were implemented on real parallel computers to produce new scientific results. It investigates issues of fine-grained parallelism relevant for future supercomputers with particular emphasis on hypercube architecture. The authors describe how they used an experimental approach to configure different massively parallel machines, design and implement basic system software, and develop algorithms for frequently used mathematical computations. They also devise performance models, measure the performance characteristics of several computers, and create a high-performance computing facility based exclusively on parallel computers. By addressing all issues involved in scientific problem solving, Parallel Computing Works! provides valuable insight into computational science for large-scale parallel architectures. For those in the sciences, the findings reveal the usefulness of an important experimental tool. Anyone in supercomputing and related computational fields will gain a new perspective on the potential contributions of parallelism. Includes over 30 full-color illustrations.
Advancements in microprocessor architecture, interconnection technology, and software development have fueled rapid growth in parallel and distributed computing.
With a little help, you can create software that maximizes both speed and efficiency. About the book Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness.
In: Proceedings of the fourth SIAM conference on parallel processing for scientific computing. SIAM, Philadelphia, pp 237-244 Cote SI (1991) Solving partial differential equations on a MIMD hypercube: fast Poisson solvers and the ...
This second edition, while retaining the general structure of the earlier book, has added two new chapters, ‘Core Level Parallel Processing’ and ‘Grid and Cloud Computing’ based on the emergence of parallel computers on a single ...
This book offers an overview of some of the most prominent parallel programming models used in high-performance computing and supercomputing systems today.
This book constitutes the proceedings of the 16th International Conference on Parallel Computing Technologies, PaCT 2021, which was held during September 13-18, 2021.
The programmer must be aware of the communication and data dependencies of the algorithm or application. This book provides the techniques to explore the possible ways to program a parallel computer for a given application.
Performance analysis tools allow application developers to identify and characterize the inefficiencies that cause ... the same HPC application running on two clusters, based respectively on Intel Haswell and Arm Cortex-A57 CPUs.
[129] L. R. Scott, J. M. Boyle, and B. Bagheri. Distributed data structures for scientific ... [136] Marc Snir, Steve W. Otto, Steven Huss-Lederman, David W. Walker, and Jack Dongarra. MPI: The Complete Reference. MIT Press, 1995.
Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science.