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About 10,000 papers for “Parallel computing”

Parallel computing

Alik Ismail‐Zadeh, Paul Tackley · Cambridge University Press eBooks · 2010

This chapter introduces only the basics of parallel computing and does not intend to cover all aspects of this topic. The major challenge in writing this chapter was to keep up with the progress in computer science, which, compared with mathematics and computational methods, is a rapidly evolving discipline, and many current approaches to parallel computing may become almost useless in about a decade or so. Meanwhile many geodynamic models cannot be solved today on a single processor because of memory or time restrictions, and hence parallel computers should be employed to run the models. Researchers dealing with computational geodynamics should know at least the basics of parallel coding and computing, and that motivated us to write this chapter. We discuss here the principal differences between sequential and parallel computing, shared and distributed memory, introduce a domain decomposition approach, message passing and MPI, analyse the cost of parallel processing, and present simple examples of codes for parallel computing. We refer the reader to the books and journals on parallel computing where the topic is described in much detail (see, for example, Lipovski and Malek, 1987; Crichlow, 1988; Fox et al., 1988; Gibbons and Rytter, 1988; Fountain, 1994; Foster, 1995; Hord, 1999; Roosta, 2000; Snyder and Lin, 2008; Barney, 2009; Elsevier's Parallel Computing Journal and Journal of Parallel and Distributed Computing, World Scientific's International Journal of High Speed Computing).

Cited by 0

PARALLEL COMPUTING

Kapish Nautiyal, Jaishree Agrawal · 2024

Modern computational solutions now rely heavily on parallel computing, which allows for the efficient processing of complicated tasks by doing numerous operations at once. This abstract gives a general overview of parallel computing, emphasizing its importance, guiding principles, applications, and possibilities for the future. In contrast to typical sequential processing, parallel computing involves the concurrent execution of tasks across numerous processors or cores. To achieve effective use of computational resources, it makes use of a variety of parallelism techniques, including task parallelism, data parallelism, thread-level parallelism, and instruction-level parallelism. The advantages of parallel processing are extensive. It speeds up computing, improves scalability, maximizes resource use, and makes it possible to solve big problems. Scientific simulations, data analytics, artificial intelligence, image processing, and other areas find use for parallel computing. It enables scientists, engineers, and creators to take on complex problems, make data-driven choices, and progress in many sectors. Key ideas including parallel hardware architectures, programming paradigms, algorithm design considerations, difficulties, and potential solutions are explored in this abstract. Additionally, it explores cutting-edge developments like quantum, neuromorphic, and exascale computing, which have the potential to transform the parallelism landscape and pave the way for fresh innovation. Parallel computing serves as a catalyst for advancement as technology advances, allowing us to push the limits of computation, acquire deeper insights into complex systems, and open the door to potentially game-changing discoveries.

Cited by 0

Parallel Computing

Giuseppe Serazzi · 2023

Abstract The concept of parallelism of computations is very important to obtain the results required by the algorithms of big data processing applied in several domains, like ML, AI, neural networks, approximate computing, etc. In this chapter the use of and stations and of their respective generation/synchronization policies are described [12]. Three elementary models implementing different synchronization policies are analyzed. The first synchronizes the executions of n parallel tasks with similar characteristics, the second investigates the impact of the variability of the parallel task execution times on the synchronization times, and the third synchronizes on the fastest task. In spite of their simplicity, these three models can be combined with other components to implement very complex models.

Cited by 2Open access

Parallel computing

Пасікова, Т., Pasikova, T. · ELARTU (Ternopil National Technical University) · 2015

Cited by 0

Parallel Computing

Reg Kulperger, Hao Yu · Encyclopedia of Environmetrics · 2001

Abstract Parallel computing consists of a computing environment connecting many processors. A new technology of distributed processing with a more loose connection between processors is emerging. Both require some special programming methods to take advantage of the connections and communications between processors, but are well suited to certain imaging problems and other statistical problems. This article describes some of the aspects of dealing with these special programming requirements, based on our experience, as statisticians, with such technologies. The types of problems and statistical use of such computing technologies are also discussed. This article is intended to give the reader an overview of the parallel computing environment focusing on the statistical uses that can be made, as opposed to a more detailed computing or engineering description.

Cited by 0

Parallel Computing

T. J. Fountain · Cambridge University Press eBooks · 1994

This book sets out the principles of parallel computing in a way which will be useful to student and potential user alike. It includes coverage of both conventional and neural computers. The content of the book is arranged hierarchically. It explains why, where and how parallel computing is used; the fundamental paradigms employed in the field; how systems are programmed or trained; technical aspects including connectivity and processing element complexity; and how system performance is estimated (and why doing so is difficult). The penultimate chapter of the book comprises a set of case studies of archetypal parallel computers, each study written by an individual closely connected with the system in question. The final chapter correlates the various aspects of parallel computing into a taxonomy of systems.

Cited by 10

6.338 Parallel Computing Fourier Transforms and Parallel Computing

2012

Computer hardware is becoming cheap and cluster computing is becoming more popular. However, it is still not easy to program in a parallel environment. Thus Matlab*p was created to provide the easy, familiar interface of Matlab to users while still utilizing the power of a parallel environment. A module for Matlab*p that calculates FFT’s would be valuable. This module must be faster than a serial version despite the Matlab wrapper and must also experience speed-up with the number of processors used on it.

Cited by 0

Human-Parallel Computing.

Boslough, Mark B. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2017

Abstract not provided.

Cited by 0Open access