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Abstract: This paper presents a new parallel algorithm for sparse matrix factorization. This algorithm uses subforest-to-subcube mapping instead of the subtree-to-subcube mapping of another recently introduced scheme by Gupta and Kumar #13#. Asymptotically, both formulations are equally scalable on a wide range of architectures and a wide variety of problems. But the subtree-to-subcube mapping of the earlier formulation causes signi#cant load imbalance among processors, limiting overall e#ciency and... (Update)
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.... algorithm for the computation of L The parallelization of the multifrontal method is based on the algorithm presented in [11, 14]. It can best be described in terms of a simple example. Let us assume that a balanced elimination tree is given, and that we want to use 4...
...such advances will at the very least have to await possible improvements in parallel general sparse matrix factoring. In this regard, [24] holds some promise) What we have shown here is that parallel dense simplex methods are neither trivial to implement nor completely...
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BibTeX entry: (Update)
George Karypis and Vipin Kumar. A high performance sparse Cholesky factorization algorithm for scalable parallel computers. Technical report TR 94-41, Department of Computer Science, University of Minnesota, Minneapolis, MN, 1994. http://citeseer.ist.psu.edu/karypis94high.html More
@techreport{ george94high,
author = "Karypis, George and Kumar, Vipin",
title = "{A} {H}igh {P}erformance {S}parse {C}holesky {F}actorization {A}lgorithm for {S}calable {P}arallel {C}omputers",
number = "94-41",
year = "94",
url = "citeseer.ist.psu.edu/karypis94high.html" }
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