ParILUT---A New Parallel Threshold ILU Factorization
نویسندگان
چکیده
منابع مشابه
A Parallel Multistage ILU Factorization Based on a Hierarchical Graph Decomposition
PHIDAL (Parallel Hierarchical Interface Decomposition ALgorithm) is a parallel incomplete factorization method which exploits a hierarchical interface decomposition of the adjacency graph of the coefficient matrix. The idea of the decomposition is similar to that of the well-known wirebasket techniques used in domain decomposition. However, the method is devised for general, irregularly structu...
متن کاملA New Torus-like Mapping for Parallel Sparse Matrix Factorization
In Cle93] we describe a new mapping of sparse matrices to the processors of a distributed memory parallel computer, called the sparse torus wrap mapping (STWM), designed to reduce the volume of interprocessor communication during the Cholesky factorization A = LL T. The mapping combines the advantages of the so-called dense torus wrap mapping (DTWM) Ash91b] developed for dense matrix factorizat...
متن کاملILUT: A dual threshold incomplete LU factorization
In this paper we describe an Incomplete LU factorization technique based on a strategy which combines two heuristics. This ILUT factorization extends the usual ILU(0) factorization without using the concept of level of ll-in. There are two traditional ways of developing incomplete factorization preconditioners. The rst uses a symbolic factorization approach in which a level of ll is attributed ...
متن کاملa new type-ii fuzzy logic based controller for non-linear dynamical systems with application to 3-psp parallel robot
abstract type-ii fuzzy logic has shown its superiority over traditional fuzzy logic when dealing with uncertainty. type-ii fuzzy logic controllers are however newer and more promising approaches that have been recently applied to various fields due to their significant contribution especially when the noise (as an important instance of uncertainty) emerges. during the design of type- i fuz...
15 صفحه اولA New Approach to Parallel Sparse Cholesky Factorization on Distributed Memory Parallel Computers
Nowadays, programming distributed memory parallel computers (DMPCs) evokes the \no pain, no gain" idea. That is, for a given problem to be solved in parallel, the message passing programming model involves distributing the data and the computations among the processors. While this can be easily feasible for well structured problems, it can become fairly hard on unstructured ones, like sparse ma...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Scientific Computing
سال: 2018
ISSN: 1064-8275,1095-7197
DOI: 10.1137/16m1079506