Parallel Algorithms and Subcube Embedding on a Hypercube
Copyright policy )Parallel Algorithms and Subcube Embedding on a Hypercube
The authors investigate embedding strategies for a hypercube multiprocessor for three fundamental algorithms: Gaussian elimination with partial pivoting, QR factorization with column pivoting and multiple least squares updating. Results are presented for the Intel iPSC/2 and iPSC/860 hypercube multiprocessors.
Numerical solutions to overdetermined systems, pseudoinverses, parallel computing, Gaussian elimination with partial pivoting, multiple least squares updating, hypercube multiprocessor, QR factorization with column pivoting, Parallel numerical computation, Direct numerical methods for linear systems and matrix inversion, Orthogonalization in numerical linear algebra
Numerical solutions to overdetermined systems, pseudoinverses, parallel computing, Gaussian elimination with partial pivoting, multiple least squares updating, hypercube multiprocessor, QR factorization with column pivoting, Parallel numerical computation, Direct numerical methods for linear systems and matrix inversion, Orthogonalization in numerical linear algebra
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!