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What is Parallel Computing

Chapter 398
Encyclopedia of Information Science and Technology, Fourth Edition
Computations performed in parallel on distributed computers or processors. Parallel computing program needs a special treatment to divide computations into many sub-computations on the distributed computers or processors. Special parallel computers would need special programing technique to divide the task into many sub-tasks or to achieve the communication among processors or computers if needed.
Published in Chapter:
Computer-Assisted Parallel Program Generation
Shigeo Kawata (Utsunomiya University, Japan)
Copyright: © 2018 |Pages: 11
DOI: 10.4018/978-1-5225-2255-3.ch398
Abstract
Parallel computation is widely employed in scientific researches, engineering activities and product development. Parallel program writing itself is not always a simple task depending on problems solved. Large-scale scientific computing, huge data analyses and precise visualizations, for example, would require parallel computations, and the parallel computing needs the parallelization techniques. In this Chapter a parallel program generation support is discussed, and a computer-assisted parallel program generation system P-NCAS is introduced. Computer assisted problem solving is one of key methods to promote innovations in science and engineering, and contributes to enrich our society and our life toward a programming-free environment in computing science. Problem solving environments (PSE) research activities had started to enhance the programming power in 1970's. The P-NCAS is one of the PSEs; The PSE concept provides an integrated human-friendly computational software and hardware system to solve a target class of problems.
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Chapter 14
Modeling Big Data Analytics with a Real-Time Executable Specification Language
Computation of several sub-tasks concurrently to solve a large or complex task.
Published in Chapter: Modeling Big Data Analytics with a Real-Time Executable Specification Language; From: Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence
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Chapter 616
On-Chip Networks for Modern Large-Scale Chips
The discipline about programming and designing systems to use multiple processing cores for a single application.
Published in Chapter: On-Chip Networks for Modern Large-Scale Chips; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 350
Recent Trends in Parallel Computing
The use of multiple computers (processors, cores) working together to solve a common problem.
Published in Chapter: Recent Trends in Parallel Computing; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 17
Revealing Concepts of a Cloud Deployment Model: A Semantic Exploration of a New Generation of the Cloud
A computing architecture where multiple processors or cores work together to solve a problem by dividing it into smaller tasks that can be processed simultaneously.
Published in Chapter: Revealing Concepts of a Cloud Deployment Model: A Semantic Exploration of a New Generation of the Cloud; From: Emerging Trends in Cloud Computing Analytics, Scalability, and Service Models
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Chapter 26
Overview of Cellular Computing-Basic Principles and Applications
A type of computation which facilitates simultaneous execution of different tasks which are primarily sub-tasks of a larger task. The main aim of parallel computing is improvement of total execution time by distributing parts of a task to separate computing nodes or cores from a single such computing unit.
Published in Chapter: Overview of Cellular Computing-Basic Principles and Applications; From: Handbook of Research on Natural Computing for Optimization Problems
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Chapter 8
Big Data and Web Intelligence for Condition Monitoring: A Case Study on Wind Turbines
A computation technique consisting on the concurrent execution of subtasks for the accomplishment of a bigger task.
Published in Chapter: Big Data and Web Intelligence for Condition Monitoring: A Case Study on Wind Turbines; From: Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence
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Chapter 130
Parallel Architectures for MEDLINE Search
The execution of a single task using multiple processors in order to speed up the task. In parallel computing, a single task may be divided into multiple subtasks that are assigned to the available processors. The results from these subtasks often are merged together to complete the task
Published in Chapter: Parallel Architectures for MEDLINE Search; From: Encyclopedia of Healthcare Information Systems
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Chapter 85
Grid Computing for Social Science
Data processing by a cluster of closely coupled processors.
Published in Chapter: Grid Computing for Social Science; From: Encyclopedia of Networked and Virtual Organizations
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Chapter 6
Parallel, Distributed, and Grid-Based Data Mining: Algorithms, Systems, and Applications
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Chapter 35
Image Partitioning on Spiral Architecture
Parallel computing is a form of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently .
Published in Chapter: Image Partitioning on Spiral Architecture; From: Handbook of Research on Scalable Computing Technologies
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Chapter 23
Nature Inspired Parallel Computing
The term parallel computing refers to the use of (parallel) supercomputers and computer clusters. Parallel computing is a form of computing in which many instructions are carried out simultaneously. It operates on the principle that large problems can almost always be divided into smaller ones, which may be solved concurrently (“in parallel”). It has been used for many years, but interest in it has become greater in recent years due to physical constraints preventing frequency scaling. Parallel computing exists in several different forms, bit-level parallelism, instruction-level parallelism, data parallelism, and task parallelism. Parallelism is a primary method for accelerating the total power of a supercomputer. Computational physics applications have been the primary drivers in the development of parallel computing over the last 20 years. Languages (e.g., Fortran and C) and libraries (e.g., message passing interface (MPI) and linear algebra libraries, i.e., LAPACK) allow the programmer to access or expose parallelism in a variety of standard ways.
Published in Chapter: Nature Inspired Parallel Computing; From: Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies
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Chapter 22
Improving Energy-Efficiency of Computational Grids via Scheduling
Also known as “parallel computation”, which represents parallel program execution or parallel file/data processing; similar to “high-performance computing”, “cluster computing” and “grid computing”; associated in the manuscript with “parallel computing“, “parallel scheduling”, “parallel applications”, and “parallel execution”.
Published in Chapter: Improving Energy-Efficiency of Computational Grids via Scheduling; From: Handbook of Research on P2P and Grid Systems for Service-Oriented Computing: Models, Methodologies and Applications
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