1 edition of **Parallel processing for some large-scale network optimization problems** found in the catalog.

Parallel processing for some large-scale network optimization problems

- 145 Want to read
- 7 Currently reading

Published
**1985**
by U.S. Dept. of Transportation, Office of the Secretary of Transportation, National Technical Information Service [distributor] in Washington, D.C, Springfield, VA
.

Written in English

- Parallel processing (Electronic computers),
- Network analysis (Planning)

**Edition Notes**

Series | University research results |

Contributions | United States. Dept. of Transportation. University Research Program, Washington State University. Dept. of Computer Science |

The Physical Object | |
---|---|

Pagination | 140 p. in various pagings : |

Number of Pages | 140 |

ID Numbers | |

Open Library | OL14941330M |

This book constitutes the proceedings of the 17th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP , held in Helsinki, Finland, in August The 25 full papers presented were carefully reviewed and selected from submissions. () Second-Order Multiplier Update Calculations for Optimal Control Problems and Related Large Scale Nonlinear Programs. SIAM Journal on Optimization .

"This book presents a domain that arises where two different branches of science, namely parallel computations and the theory of constrained optimization, intersect with real life problems. This domain, called parallel optimization, has been developing rapidly under the . Other articles where Parallel distributed processing is discussed: artificial intelligence: Conjugating verbs: Another name for connectionism is parallel distributed processing, which emphasizes two important features. First, a large number of relatively simple processors—the neurons—operate in parallel. Second, neural networks store information in a distributed fashion, with each.

The short answer to your question is that there is no conventional way to write pseudocode for parallel programming. This is due to there being a variety of ways to do parallel programming, in terms of different parallel architectures (e.g. SMPs, GPUs, clusters, and other exotic systems) and parallel programming approaches. He is particularly interested in exploring new performance limits at the network layer by exploiting advances at the physical layer. In recent years, he has been actively working on real-time optimization based on GPU platform and solving large-scale complex optimization problems for .

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Get this from a library. Parallel processing for some large-scale network optimization problems. [United States. Department of Transportation.

University Research Program.; Washington State University. Department of Computer Science.;]. Related work on parallel processing of discrete problems may be found in [24], and in[25]where the question of reducing CPU time of Las Vegas type algorithms is considered in both serial and.

In this paper the technical aspects concerning an efficient implementation of parallel methods for solving large-scale network flow optimization problems are discussed.

In particular, the attention will be focused to the evaluation of the numerical performance of different synchronous implementations of the relaxation method on shared-memory Cited by: 1.

Description: This book offers a unique pathway to methods of parallel optimization by introducing parallel computing ideas into both optimization theory and into some numerical algorithms for large-scale optimization problems. The three parts of the book bring together relevant theory, careful study of algorithms, and modeling of significant.

[Show full abstract] Availability of parallel computers has created substantial interest in exploring the use of parallel processing for solving discrete optimization problems. This article. Massively Parallel Processing Applications and Development Select Massively parallel domain decomposition algorithms for some aerodynamics problems.

Book chapter Full text access. However, due to the large scale and the strong variability of the phenomena involved, present knowledge still has a fragmentary character.

MathWorks parallel computing products help you harness a variety of computing resources for solving your computationally intensive problems. You can accelerate the processing of repetitive computations, process large amounts of data, or offload processor-intensive tasks on a computing resource of your choice—multicore computers, GPUs, or larger resources such as computer clusters and cloud.

Systolic architectures offer the competence to uphold the high-throughput capacity requirement. Multi-dimensional image processing algorithms, video streaming, nonlinear optimization problems and decision based algorithms are a few of many algorithms that are computationally demanding and can be benefited by implementing systolic arrays.

This book constitutes the workshop proceedings of the 18th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PPheld in Guangzhou, China, in November The 24 full papers presented were carefully selected and reviewed from numerous submissions to the two following workshops.

This book offers a unique pathway to methods of parallel optimization by introducing parallel computing ideas into both optimization theory and into some numerical algorithms for large-scale optimization problems. The three parts of the book bring together relevant theory, careful study of algorithms, and modeling of significant real world.

Types of parallel processing. There are multiple types of parallel processing, two of the most commonly used types include SIMD and MIMD. SIMD, or single instruction multiple data, is a form of parallel processing in which a computer will have two or more processors follow the same instruction set while each processor handles different data.

The book focuses on parallel optimization methods for large-scale constrained optimization problems and structured linear problems [It] covers a vast portion of parallel optimization, though full coverage of this domain, as the authors admit, goes far beyond the capacity of a single monograph.

Parallel processing for scanning genomic data-bases (D. Lavenier, J.-L. Pacherie). Application of a multi-processor system for recognition of EEG-activities in amplitude, time and space in real-time. Roscher et al.). Solving large-scale network transportation problems on a cluster of workstations (P.

Beraldi, L. Grandinetti, F. Guerriero). This book offers a unique pathway to methods of parallel optimization by introducing parallel computing ideas into both optimization theory and into some numerical algorithms for large-scale optimization problems.

The three parts of the book bring together relevant theory, careful study of algorithms, and modeling of significant real world Reviews: 2.

This book contains papers presented at the Workshop on Parallel Processing of Discrete Optimization Problems held at DIMACS in April The contents cover a wide spectrum of the most recent algorithms and applications in parallel processing of discrete optimization and related problems.

Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time.

There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters.

High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—enable you to parallelize MATLAB ® applications without CUDA or MPI programming.

Parallel processing for AI problems is of great current interest because of its potential for alleviating the computational demands of AI procedures. The articles in this book consider parallel processing for problems in several areas of artificial intelligence: image processing, knowledge representation in semantic networks, production rules.

This paper reports on a new parallel implementation of the primal simplex method for minimum cost network flow problems that decomposes both the pivoting and pricing operations. The self-scheduling approach is flexible and efficient; its implementation is close in speed to the best serial code when using one processor, and is capable of.

Parallel computing, a paradigm in computing which has multiple tasks running simultaneously, might contain what is known as an embarrassingly parallel workload or problem (also called perfectly parallel, delightfully parallel or pleasingly parallel).An embarrassingly parallel task can be considered a trivial case - little or no manipulation is needed to separate the problem into a number of.

PARALLEL DISTRIBUTED PROCESSING Explorations in the Microstructure of Cognition Volume 1: Foundations lel network to perform cooperative searches for good solutions to prob- lems.

The basic idea is simple: The weights on the connections Many optimization problems can be cast in a framework known as.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 Reviews: 3.specific problems, e.g.

machine learning (ML) and stream data processing. Recently, large-scale ML systems have been actively researched and developed, since scal-ability is one of the bottlenecks now for ML applications. Representative ML systems include the graph-centric GraphLab [62], and the ML-centric Petuum [81].

S4 [68].