HumanParallel Computing.
Abstract
Abstract not provided.
 Authors:
 Publication Date:
 Research Org.:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA)
 OSTI Identifier:
 1374688
 Report Number(s):
 SAND20167476PE
646339
 DOE Contract Number:
 AC0494AL85000
 Resource Type:
 Conference
 Resource Relation:
 Conference: Proposed for presentation at the DSO Proposers Day held June 2223, 2016 in Arlington, VA.
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Boslough, Mark B. HumanParallel Computing.. United States: N. p., 2016.
Web.
Boslough, Mark B. HumanParallel Computing.. United States.
Boslough, Mark B. 2016.
"HumanParallel Computing.". United States.
doi:. https://www.osti.gov/servlets/purl/1374688.
@article{osti_1374688,
title = {HumanParallel Computing.},
author = {Boslough, Mark B.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 8
}
Other availability
Please see Document Availability for additional information on obtaining the fulltext document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Highly parallel computing of linear equations on the matrixbroadcastmemory connected array processor system
The authors describe an efficient method for computing a set of n linear simultaneous equations in o(n) time, on the proposed array processor system. The proposed system is composed of a large number of microprocessors and its layout is p*q square. The microprocessors (hereafter called processing elements, or PEs) are connected to the row and columnwise broadcast memories. 2 references. 
Modelling of the parallel resolution of a numerical problem on a locally distributed computing system
The authors investigate the behaviour of a parallel computing system, consisting of a number of independent processors, which execute in parallel the numerical solution of a partial differential equation. As a measure of system they consider the speed gain obtained per iteration step on the parallel processors with respect to the oneprocessor execution of the equivalent task. For the performance evaluation of the parallel system they use a probabilistic model of the computation scheme, based upon several main assumptions concerning computation and communication times. They provide the exact solution of the model in the case of two processors and thenmore » 
Parallel processing for scientific computing; Proceedings of the Third SIAM Conference, Los Angeles, CA, Dec. 14, 1987
The present conference discusses topics in the fields of matrix computations, numerical methods, differential equations, parallel processing applications in science, computer languages and software systems for parallel processing, and parallel computer architectures. Attention is given to Gaussian techniques on shared memory multiprocessors, a novel parallel algorithm for linear triangular systems, parallel rapid elliptic solvers, parallel multivariate numerical integration, and the implementation of the acceptancerejection method on parallel processors. Also discussed are a parallel multilevel FEM with hierarchical basis functions, the parallel processing of a domain decomposition method, the parallelization of adaptive grid domain mappings, the automated decomposition of FEM meshesmore » 
Proceedings of the 2nd conference on parallel processing for scientific computing
This book contains papers presented at a conference on parallel processing for scientific computing. Topics included are the following: WY representation of products of household matrices; a domain decomposed fast Poisson solver on a rectangle; multiprocessor fast fourier transform methods; asynchronous relaxations for the numerical solution of differential equations by parallel processors; solving equations of motion on a vertical tree machine; design and analysis of parallel Monte Carlo algorithms; adapting a navierstokes code to the ICLDap; and hypercube algorithms and implementations.