**Author**: Seyed H Roosta

**Publisher:** Springer Science & Business Media

**ISBN:** 0387987169

**Category:** Computers

**Page:** 590

**View:** 583

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## Parallel Processing and Parallel Algorithms

Motivation It is now possible to build powerful single-processor and multiprocessor systems and use them efficiently for data processing, which has seen an explosive ex pansion in many areas of computer science and engineering. One approach to meeting the performance requirements of the applications has been to utilize the most powerful single-processor system that is available. When such a system does not provide the performance requirements, pipelined and parallel process ing structures can be employed. The concept of parallel processing is a depar ture from sequential processing. In sequential computation one processor is in volved and performs one operation at a time. On the other hand, in parallel computation several processors cooperate to solve a problem, which reduces computing time because several operations can be carried out simultaneously. Using several processors that work together on a given computation illustrates a new paradigm in computer problem solving which is completely different from sequential processing. From the practical point of view, this provides sufficient justification to investigate the concept of parallel processing and related issues, such as parallel algorithms. Parallel processing involves utilizing several factors, such as parallel architectures, parallel algorithms, parallel programming lan guages and performance analysis, which are strongly interrelated. In general, four steps are involved in performing a computational problem in parallel. The first step is to understand the nature of computations in the specific application domain.
## Introduction to Parallel Algorithms

Parallel algorithms Made Easy The complexity of today's applications coupled with the widespread use of parallel computing has made the design and analysis of parallel algorithms topics of growing interest. This volume fills a need in the field for an introductory treatment of parallel algorithms-appropriate even at the undergraduate level, where no other textbooks on the subject exist. It features a systematic approach to the latest design techniques, providing analysis and implementation details for each parallel algorithm described in the book. Introduction to Parallel Algorithms covers foundations of parallel computing; parallel algorithms for trees and graphs; parallel algorithms for sorting, searching, and merging; and numerical algorithms. This remarkable book: * Presents basic concepts in clear and simple terms * Incorporates numerous examples to enhance students' understanding * Shows how to develop parallel algorithms for all classical problems in computer science, mathematics, and engineering * Employs extensive illustrations of new design techniques * Discusses parallel algorithms in the context of PRAM model * Includes end-of-chapter exercises and detailed references on parallel computing. This book enables universities to offer parallel algorithm courses at the senior undergraduate level in computer science and engineering. It is also an invaluable text/reference for graduate students, scientists, and engineers in computer science, mathematics, and engineering.
## Algorithms and Architectures for Parallel Processing, Part II

This two volume set LNCS 7016 and LNCS 7017 constitutes the refereed proceedings of the 11th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2011, held in Melbourne, Australia, in October 2011. The second volume includes 37 papers from one symposium and three workshops held together with ICA3PP 2011 main conference. These are 16 papers from the 2011 International Symposium on Advances of Distributed Computing and Networking (ADCN 2011), 10 papers of the 4th IEEE International Workshop on Internet and Distributed Computing Systems (IDCS 2011), 7 papers belonging to the III International Workshop on Multicore and Multithreaded Architectures and Algorithms (M2A2 2011), as well as 4 papers of the 1st IEEE International Workshop on Parallel Architectures for Bioinformatics Systems (HardBio 2011).
## Introduction to Parallel Processing

This original text provides comprehensive coverage of parallel algorithms and architectures, beginning with fundamental concepts and continuing through architectural variations and aspects of implementation. Unlike the authors of similar texts, Professor Parhami reviews the circuit model and problemdriven parallel machines, variants of mesh architectures, and composite and hierarchical systems, among other subjects. With its balanced treatment of theory and practical designs, classtested lecture material and problems, and helpful case studies, the book is suited to graduate and upperlevel undergraduate students of advanced architecture or parallel processing.
## Parallel Processing and Parallel Algorithms

Motivation It is now possible to build powerful single-processor and multiprocessor systems and use them efficiently for data processing, which has seen an explosive ex pansion in many areas of computer science and engineering. One approach to meeting the performance requirements of the applications has been to utilize the most powerful single-processor system that is available. When such a system does not provide the performance requirements, pipelined and parallel process ing structures can be employed. The concept of parallel processing is a depar ture from sequential processing. In sequential computation one processor is in volved and performs one operation at a time. On the other hand, in parallel computation several processors cooperate to solve a problem, which reduces computing time because several operations can be carried out simultaneously. Using several processors that work together on a given computation illustrates a new paradigm in computer problem solving which is completely different from sequential processing. From the practical point of view, this provides sufficient justification to investigate the concept of parallel processing and related issues, such as parallel algorithms. Parallel processing involves utilizing several factors, such as parallel architectures, parallel algorithms, parallel programming lan guages and performance analysis, which are strongly interrelated. In general, four steps are involved in performing a computational problem in parallel. The first step is to understand the nature of computations in the specific application domain.
## Parallel Computing

Parallel Computing: Methods, Algorithms and Applications presents a collection of original papers presented at the international meeting on parallel processing, methods, algorithms, and applications at Verona, Italy in September 1989.
## Introduction to Parallel Computing

Advancements in microprocessor architecture, interconnection technology, and software development have fueled rapid growth in parallel and distributed computing. However, this development is only of practical benefit if it is accompanied by progress in the design, analysis and programming of parallel algorithms. This concise textbook provides, in one place, three mainstream parallelization approaches, Open MPP, MPI and OpenCL, for multicore computers, interconnected computers and graphical processing units. An overview of practical parallel computing and principles will enable the reader to design efficient parallel programs for solving various computational problems on state-of-the-art personal computers and computing clusters. Topics covered range from parallel algorithms, programming tools, OpenMP, MPI and OpenCL, followed by experimental measurements of parallel programs’ run-times, and by engineering analysis of obtained results for improved parallel execution performances. Many examples and exercises support the exposition.
## Algorithms and Architectures for Parallel Processing

The two-volume set LNCS 11944-11945 constitutes the proceedings of the 19th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2019, held in Melbourne, Australia, in December 2019. The 73 full and 29 short papers presented were carefully reviewed and selected from 251 submissions. The papers are organized in topical sections on: Parallel and Distributed Architectures, Software Systems and Programming Models, Distributed and Parallel and Network-based Computing, Big Data and its Applications, Distributed and Parallel Algorithms, Applications of Distributed and Parallel Computing, Service Dependability and Security, IoT and CPS Computing, Performance Modelling and Evaluation.
## Introduction to Parallel Computing

A complete source of information on almost all aspects of parallel computing from introduction, to architectures, to programming paradigms, to algorithms, to programming standards. It covers traditional Computer Science algorithms, scientific computing algorithms and data intensive algorithms.
## Opportunities and Constraints of Parallel Computing

At the initiative of the IBM Almaden Research Center and the National Science Foundation, a workshop on "Opportunities and Constraints of Parallel Computing" was held in San Jose, California, on December 5-6, 1988. The Steering Committee of the workshop consisted of Prof. R. Karp (University of California at Berkeley), Prof. L. Snyder (University of Washington at Seattle), and Dr. J. L. C. Sanz (IBM Almaden Research Center). This workshop was intended to provide a vehicle for interaction for people in the technical community actively engaged in research on parallel computing. One major focus of the workshop was massive parallelism, covering theory and models of computing, algorithm design and analysis, routing architectures and interconnection networks, languages, and application requirements. More conventional issues involving the design and use of parallel computers with a few dozen processors were not addressed at the meeting. A driving force behind the realization of this workshop was the need for interaction between theoreticians and practitioners of parallel computation. Therefore, a group of selected participants from the theory community was invited to attend, together with well-known colleagues actively involved in parallelism from national laboratories, government agencies, and industry.
## Inherently Parallel Algorithms in Feasibility and Optimization and their Applications

The Haifa 2000 Workshop on "Inherently Parallel Algorithms for Feasibility and Optimization and their Applications" brought together top scientists in this area. The objective of the Workshop was to discuss, analyze and compare the latest developments in this fast growing field of applied mathematics and to identify topics of research which are of special interest for industrial applications and for further theoretical study. Inherently parallel algorithms, that is, computational methods which are, by their mathematical nature, parallel, have been studied in various contexts for more than fifty years. However, it was only during the last decade that they have mostly proved their practical usefulness because new generations of computers made their implementation possible in order to solve complex feasibility and optimization problems involving huge amounts of data via parallel processing. These led to an accumulation of computational experience and theoretical information and opened new and challenging questions concerning the behavior of inherently parallel algorithms for feasibility and optimization, their convergence in new environments and in circumstances in which they were not considered before their stability and reliability. Several research groups all over the world focused on these questions and it was the general feeling among scientists involved in this effort that the time has come to survey the latest progress and convey a perspective for further development and concerted scientific investigations. Thus, the editors of this volume, with the support of the Israeli Academy for Sciences and Humanities, took the initiative of organizing a Workshop intended to bring together the leading scientists in the field. The current volume is the Proceedings of the Workshop representing the discussions, debates and communications that took place. Having all that information collected in a single book will provide mathematicians and engineers interested in the theoretical and practical aspects of the inherently parallel algorithms for feasibility and optimization with a tool for determining when, where and which algorithms in this class are fit for solving specific problems, how reliable they are, how they behave and how efficient they were in previous applications. Such a tool will allow software creators to choose ways of better implementing these methods by learning from existing experience.
## Algorithms and Parallel Computing

There is a software gap between the hardware potential and the performance that can be attained using today's software parallel program development tools. The tools need manual intervention by the programmer to parallelize the code. Programming a parallel computer requires closely studying the target algorithm or application, more so than in the traditional sequential programming we have all learned. 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.