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About 10,000 papers for “Parallel computing”

PARALLEL COMPUTING

Mrs. Anush Sharma · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2024

Parallel computing is a computational technique in which two or more tasks or instructions can be processed concurrently by means of several processors resulting into enhanced computational efficiency as well as decreased time for solving complex problems. As an approach to solve a particular problem, parallel computing is an attempt that decomposes a problem into constituent subproblems to be solved concurrently; it uses multiple cores, hardware systems to gain advantage in speed The use of parallel computing is prominent in sciences that call for massive computations such as simulations, data mining, artificial intelligence and HPC. Key Words- Parallel computing, Concurrent processing, Computational efficiency, Problem decomposition, Subproblems, Simulations, Data mining, Artificial intelligence, High-performance computing (HPC), Multiple processors, Multiple cores, Hardware systems

Cited by 1Open access

Parallel Computing

D.J. Wallace · 2020

This chapter provides a basic introduction to the present status of parallel computing, and to some of the key issues in it. The three forms of parallelism which are common in many problems are: event parallelism; geometric parallelism; and algorithmic parallelism. Parallel systems have some inbuilt advantages in cost effectiveness; they typically do not demand large numbers of special purpose chips; are amenable to standard very large scale integrated design methods; and the replication of components provides the opportunity of economies of scale even in a single machine. The present state of system and compiler technology is such that in all supercomputer or novel architecture systems, although to very different degrees, considerable effort must be made by the programmer if a performance approaching the machine’s peak is to be attained. The string dynamics is intrinsically parallel, and has been implemented by a straightforward ‘domain decomposition’ on the computing Surface.

Cited by 0

Parallel computing

Kalinov, A., Lastovetsky, A., Robert, Yves · HAL (Le Centre pour la Communication Scientifique Directe) · 2005

Cited by 0

Parallel Computing

Purdue E-pubs, K. N. Pantazopoulos, Elias N. Houstis et al. · 1997

This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact epubs@purdue.edu for additional information.

Cited by 0

Parallel computing

Dominik Marx, Jürg Hutter · Cambridge University Press eBooks · 2009

Ab initio molecular dynamics calculations call for substantial computer resources. Memory and CPU time requirements make it necessary to run projects on the largest computers available. Today and in the foreseeable future, these high-end resources are provided exclusively by parallel computers. There are many different types of parallel architectures available, differing in their memory access system and their communication system. In addition, widely different performances are seen for the amount of data that can be sent from one processor to another per unit time (bandwidth) and the minimal time needed to send a small message (latency). Further-more, different parallel programming paradigms are supported. In order to have a portable code that can be used on most of the current computer architectures, CPMD was programmed using standard communication libraries and making no assumption about the topology of the processor network and memory access system. Since this approach is also the basis of other general-purpose ab initio molecular dynamics codes, it will serve in this book as the specific case in order to explain in general how to design and code up a highly efficient parallel program.

Cited by 0

PARALLEL COMPUTING

Dr Ani Brown Mary N, Dr Anne Wincy, Dr Abbiramy V S et al. · 2024

Parallel computing is applied for the development of breaking down bigger problems into smaller. These parts are independent, which can be implemented instantaneously by multiple processors collaborating using shared memory. Parallel Systems are systems designed with multiple processors to execute the processes. Concurrent systems are schemes that are built from a set of independent components which perform their operations at the same instant of time. These components interact in a controlled manner so that the interactions do not produce unwanted issues such as deadlock and resource starvation. The reckless computers in the world use from top to bottom level parallelism. These systems are leading to novel scientific findings.

Cited by 0

Parallel Computing

National Cancer Institute · Definitions · 2020

Model for high performance computing in which a problem is solved by multiple processors working cooperatively and simultaneously.

Cited by 1Open access

Parallel computing

David Davidson · IEEE Potentials · 1995

Parallel processing is the harnessing of multiple processors to work on the same problem. The aim is to speed up the computational process, ideally by the number of processors used. Parallel processing is increasingly emerging as the key to very-high-speed computation. This article is an introductory overview emphasizing the use of parallel computers for computational engineering.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 0

Parallel Computing

Daniel S. Priece · 2008

The rapid rate at which technology is evolving has led to a need for more sophisticated techniques that can take full advantage of the hardware available. Traditionally, software has lagged behind hardware and there is a continuous need to maximize software performance on multiprocessor systems. This paper reviews the fundamentals of parallel computing with a focus on implicit, or automatic, parallelization. We discuss different software techniques as well as restrictions that arise when creating parallel code. 1.

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Parallel Computing

X. Cai, E. Acklam, H. P. Langtangen et al. · 2007

Abstract. Large-scale parallel computations are more common than ever, due to the increasing availability of multi-processor systems. However, writing parallel software is often a complicated and error-prone task. To relieve Diffpack users of the tedious and low-level technical details of parallel programming, we have designed a set of new software modules, tools, and programming rules, which will be the topic of the present chapter. 1 Introduction to Parallel Computing Parallel computing, also known as concurrent computing, refers to a group of independent processors working collaboratively to solve a large computational problem. This is motivated by the need to reduce the execution time and to utilize larger memory/storage resources. The essence of parallel

Cited by 0