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462
A Parallel Implementation of the Davidson Method for Generalized Eigenproblems
, 2009
"... We present a parallel implementation of the Davidson method for the numerical solution of largescale, sparse, generalized eigenvalue problems. The implementation is done in the context of SLEPc, the Scalable Library for Eigenvalue Problem Computations. In this work, we focus on the Hermitian versi ..."
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Cited by 5 (2 self)
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We present a parallel implementation of the Davidson method for the numerical solution of largescale, sparse, generalized eigenvalue problems. The implementation is done in the context of SLEPc, the Scalable Library for Eigenvalue Problem Computations. In this work, we focus on the Hermitian
A parallel and scalable iterative solver for sequences of dense eigenproblems arising
 in FLAPW. Lecture Notes in Computer Science, Parallel Processing and Applied Mathematics(8385):395–406
, 2014
"... ar ..."
Parallel Solution of Generalized Complex Symmetric Eigenproblems
"... We investigate a method for efficiently solving a complex symmetric (nonHermitian) generalized eigenvalue problem (EVP) Ax = λBx in parallel. An evaluation featuring up to 1024 CPUcores evidences encouraging runtime behavior. 1 ..."
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We investigate a method for efficiently solving a complex symmetric (nonHermitian) generalized eigenvalue problem (EVP) Ax = λBx in parallel. An evaluation featuring up to 1024 CPUcores evidences encouraging runtime behavior. 1
Approximate inverse preconditioning in the parallel solution of sparse eigenproblems
"... A preconditioned scheme for solving sparse symmetric eigenproblems is proposed. The solution strategy relies upon the DACG algorithm, which is a Preconditioned Conjugate Gradient algorithm for minimizing the Rayleigh Quotient. A comparison with the well established ARPACK code, shows that when a sma ..."
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Cited by 22 (11 self)
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of the DACG algorithm. Numerical tests account for the high degree of parallelization attainable on a Cray T3E machine and confirm the satisfactory scalability properties of the algorithm. A final comparison with PARPACK shows the (relative) higher efficiency of AINVDACG. KEY WORDS generalized eigenproblem
Parallel RFSAIBFGS preconditioners for large symmetric eigenproblems
, 2013
"... In this paper we propose a parallel preconditioner for the Newton method in the computation of the leftmost eigenpairs of large and sparse symmetric positive definite matrices. A sequence of preconditioners starting from an enhanced approximate inverse RFSAI [13] and enriched by a BFGSlike update f ..."
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Cited by 2 (1 self)
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In this paper we propose a parallel preconditioner for the Newton method in the computation of the leftmost eigenpairs of large and sparse symmetric positive definite matrices. A sequence of preconditioners starting from an enhanced approximate inverse RFSAI [13] and enriched by a BFGSlike update
Scan Primitives for GPU Computing
 GRAPHICS HARDWARE 2007
, 2007
"... The scan primitives are powerful, generalpurpose dataparallel primitives that are building blocks for a broad range of applications. We describe GPU implementations of these primitives, specifically an efficient formulation and implementation of segmented scan, on NVIDIA GPUs using the CUDA API.Us ..."
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Cited by 173 (9 self)
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The scan primitives are powerful, generalpurpose dataparallel primitives that are building blocks for a broad range of applications. We describe GPU implementations of these primitives, specifically an efficient formulation and implementation of segmented scan, on NVIDIA GPUs using the CUDA API
A Study on Parallel Implementation of Large Scale Eigenproblem Solver for Distributed Memory Architecture Parallel Machines
, 1998
"... In this thesis, we discuss how to implemented the eigensolver which is frequently used in science and engineering problems. There is many ways of solving eigenproblems according to the requests, e.g. obtaining all eigenvalues only, all of eigenvalues and eigenvectors, etc. This thesis treats the cas ..."
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Cited by 1 (1 self)
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the case of all eigenvalue and eigenvector problem, because this is typical problem on eigenproblem solver. Reduction of the matrix with many zero entries without changing its eigenvalue is the first important procedure for the eigenproblem. Symmetric or unsymmetric nature of the problem does not affect
ManySAT: a parallel SAT solver
 JOURNAL ON SATISFIABILITY, BOOLEAN MODELING AND COMPUTATION (JSAT)
, 2009
"... In this paper, ManySAT a new portfoliobased parallel SAT solver is thoroughly described. The design of ManySAT benefits from the main weaknesses of modern SAT solvers: their sensitivity to parameter tuning and their lack of robustness. ManySAT uses a portfolio of complementary sequential algorithms ..."
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Cited by 54 (14 self)
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algorithms obtained through careful variations of the standard DPLL algorithm. Additionally, each sequential algorithm shares clauses to improve the overall performance of the whole system. This contrasts with most of the parallel SAT solvers generally designed using the divideandconquer paradigm
Software For The Generalized Eigenproblem On Distributed Memory Architectures
"... . The generalized eigenproblem is of significant importance in several fields. Generalized eigenproblems can be very large with matrices of order greater than one million for problems arising from threedimensional finite element models. To solve such problems we are proposing a flexible software sy ..."
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. The generalized eigenproblem is of significant importance in several fields. Generalized eigenproblems can be very large with matrices of order greater than one million for problems arising from threedimensional finite element models. To solve such problems we are proposing a flexible software
An UnsymmetricPattern Multifrontal Method for Sparse LU Factorization
 SIAM J. MATRIX ANAL. APPL
, 1994
"... Sparse matrix factorization algorithms for general problems are typically characterized by irregular memory access patterns that limit their performance on parallelvector supercomputers. For symmetric problems, methods such as the multifrontal method avoid indirect addressing in the innermost loops ..."
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Cited by 153 (26 self)
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Sparse matrix factorization algorithms for general problems are typically characterized by irregular memory access patterns that limit their performance on parallelvector supercomputers. For symmetric problems, methods such as the multifrontal method avoid indirect addressing in the innermost
Results 1  10
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