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@ARTICLE{Basermann:188179,
author = {Basermann, A. and Reichel, B. and Schelthoff, C.},
title = {{P}reconditioned {CG} methods for sparse matrices on
massively parallel machines},
journal = {Parallel computing},
volume = {23},
number = {3},
issn = {0167-8191},
address = {Amsterdam [u.a.]},
publisher = {North-Holland, Elsevier Science},
reportid = {FZJ-2015-01639},
pages = {381 - 398},
year = {1997},
abstract = {Conjugate gradient (CG) methods to solve sparse systems of
linear equations play an important role in numerical methods
for solving discretized partial differential equations. The
large size and the condition of many technical or physical
applications in this area result in the need for efficient
parallelization and preconditioning techniques of the CG
method, in particular on massively parallel machines. Here,
the data distribution and the communication scheme for the
sparse matrix operations of the preconditioned CG are based
on the analysis of the indices of the non-zero elements.
Polynomial preconditioning is shown to reduce global
synchronizations considerably, and a fully local incomplete
Cholesky preconditioner is presented. On a PARAGON XP/S 10
with 138 processors, the developed parallel methods
outperform diagonally scaled CG markedly with respect to
both scaling behavior and execution time for many matrices
from real finite element applications.},
cin = {JSC},
ddc = {004},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {899 - ohne Topic (POF2-899)},
pid = {G:(DE-HGF)POF2-899},
typ = {PUB:(DE-HGF)16},
UT = {WOS:A1997XB80600007},
doi = {10.1016/S0167-8191(97)00005-7},
url = {https://juser.fz-juelich.de/record/188179},
}