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@INPROCEEDINGS{Schelthoff:200820,
author = {Schelthoff, Christof and Basermann, Achim},
title = {{P}olynomial {P}reconditioning for the {C}onjugate
{G}radient {M}ethod on {M}assively {P}arallel {S}ystems},
volume = {95/1},
address = {Clausthal-Zellerfeld},
publisher = {Institut für Informatik},
reportid = {FZJ-2015-03205},
series = {Informatik-Bericht},
pages = {150-167},
year = {1995},
comment = {Proceedings des 13. Workshops über Parallelverarbeitung},
booktitle = {Proceedings des 13. Workshops über
Parallelverarbeitung},
abstract = {A frequently used iterative algorithm for solving large,
sparse, symmetric and positiv definite systems of linear
equations is the method of conjugate gradients (CG).This
method requires one matrix-vector product and some dot
products in each iteration. Convergence is dependent on the
condition number of the coefficient matrix. So
preconditioning techniques are used to reduce the number of
iterations.In this context, polynomial preconditioning was
developed. This method decreases the total number of dot
products by reducing the total number of iterations. Of
course, some additional work has to be done for the
preconditioning. When a polynomial of degree k is used, k
matrix-vector products per iteration have to be calculated
rather than one. On scalar machines, this shift between
matrix-vector products and dot products influences the
performance of the algorithm only slightly. On massively
parallel systems, dot products require global
synchronization, while the calculation of matrix-vector
products merely results in communication with a small number
of processors. Hence, polynomial preconditioned CG seems to
scale better than CG without preconditioning. Of course,
this is not the case in general. The performance of this
preconditioner depends on several issues, e.g., the sparsity
pattern and the eigenvalue distribution of the matrix, an
efficient communication scheme for the matrix-vector
products and the time needed for global synchronization of
the specific parallel machine. The actual implementation
used here is based on Chebyshev polynomials. Performance
tests were carried out on the Intel Paragon XP/S 10 with 140
nodes at the Research Centre Jülich (KFA). The CG method
with polynomial preconditioning shows better performance and
scalability than the basic method on massively parallel
machines. Additionally there are some numerical advantages
like a higher accuracy and an increased stability.},
month = {Sep},
date = {1994-09-25},
organization = {13. Workshop über
Parallelverarbeitung, Lessach
(Austria), 25 Sep 1994 - 1 Oct 1994},
cin = {JSC / ZAM},
ddc = {004},
cid = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)VDB62},
pnm = {899 - ohne Topic (POF2-899)},
pid = {G:(DE-HGF)POF2-899},
typ = {PUB:(DE-HGF)8},
url = {https://juser.fz-juelich.de/record/200820},
}