sparse block linear solver
Reordering Strategy for Blocking Optimization in Sparse Linear Solvers
... Fig. 2.3. Three levels of nested dissection on a regular cube. (Color available online.) Inside each separator, vertices have to be ordered as well, and it is common to use techniques such as the reverse Cuthill–McKee ...
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The solution of large sparse linear systems on parallel computers using a hybrid implementation of the block Cimmino method
... direct solver and the update of the solution is iterative, which is why they are considered ...the block Cimmino method in which these projections are basically summed at each iteration and therefore can be ...
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Task-based hybrid linear solver for distributed memory heterogeneous architectures
... the sparse direct ...dense linear algebraand is now a common utility for related state-of-the-art libraries such as Plasma [2], Magma [1], DPLASMA [14], Chameleon [4] and FLAME ...Dense linear ...
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3D frequency-domain seismic modeling with a Block Low-Rank algebraic multifrontal direct solver
... a sparse impedance matrix, whose coefficients depend on frequency and subsur- face properties ...resulting linear sys- tem can be solved with a sparse direct solver based on the multifrontal ...
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Efficient 3D frequency-domain full-waveform inversion of ocean-bottom cable data with sparse block low-rank direct solver: a real data case study from the North Sea
... One advantage of the frequency domain deals with implemen- tation of attenuation in both seismic modeling and inversion (Malinowski et al., 2011), that will not generate a significant computational burden unlike in the ...
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Numerical Platon: a unified linear equation solver interface for industrial softwares
... and sparse matrices, as well as parallel direct ...or block structured and finite element space ...for sparse approximated inverse matrix computation, the Blocksolve95, or the SuperLU (se- quential, ...
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A block minimum residual norm subspace solver for sequences of multiple left and right-hand side linear systems
... of linear systems are typically available in ...of linear systems, to name a few, such as full waveform inversion problems [ 37 , 38 ], frequency response functions computation of a vibrating system over a ...
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Exploiting a Parametrized Task Graph model for the parallelization of a sparse direct multifrontal solver
... in block-columns and a Communication Avoiding method based on a 2D partitioning into tiles [18, ...dense linear algebra kernels routine for distributed systems built on top of the PaRSEC runtime ...
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Sparse Supernodal Solver Using Block Low-Rank Compression
... a Block Low-Rank (BLR) compression technique to reduce the memory footprint and/or the time-to-solution of the sparse supernodal solver P A S TI ...of sparse linear systems, which come ...
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Scheduling Trees of Malleable Tasks for Sparse Linear Algebra
... within sparse direct solvers fit the model introduced by Prasanna and Musicus in [20] we conducted an experimental study on several dense linear algebra ...in sparse linear algebra ...
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Hierarchical hybrid sparse linear solver for multicore platforms
... the block symbolic factorization has been performed, the block elimination tree and the supernode partition are ...diagonal block is computed (red block in Figure ...
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Non incremental LATIN-PGD solver for non-linear vibratoric dynamics problems
... Résumé — The LATIN-PGD method is a fast non-linear and non incremental solver generally applied in solid mechanics that use on their formulation a model reduction technique called Proper Generalize ...
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Scheduling Trees of Malleable Tasks for Sparse Linear Algebra
... basic block for the factorization of sparse ...using linear regression on the first part of the graph (p ≤ 10 for 1D partitioning, p ≤ 20 for 2D ...
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Bayesian Functional Linear Regression with Sparse Step Functions
... MSC 2010 subject classifications: Primary 62F15; secondary 62J05. Keywords: Bayesian regression, functional data, support estimate, parsimony. 1 Introduction Consider that one wants to explain the final outcome y of a ...
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Identification of switched linear systems via sparse optimization
... of sparse optimization, ...Since sparse op- timization is in general non-convex, it is classical to con- sider instead a convex ℓ 1 relaxation of this ...
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Approximate cross validation for sparse generalized linear models
... Our main contribution is to demonstrate one case in which this notion of effective dimension is helpful for approximate CV - that of f, regularized generalized lin[r] ...
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The Dynamic Latent Block Model for Sparse and Evolving Count Matrices
... As usual, we look for a way to maximize the log-likelihood in order to obtain the estimation of θ. In the co-clustering case, the EM algorithm is computationally infeasible. In this work, we have chosen to use a ...
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Estimating linear functionals of a sparse family of Poisson means
... (equal to the Euclidean norm of the vector composed of its singular values). For any matrix M, we denote by L(M) the linear functional defined as the sum of the columns of M. 2. GROUP HARD THRESHOLDING ESTIMATOR ...
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Fast and Accurate Simulation of Multithreaded Sparse Linear Algebra Solvers
... ∗ CNRS/Inria/University of Grenoble Alpes, France; firstname.lastname@imag.fr † Inria/University of Bordeaux, France; firstname.lastname@labri.fr ‡ CNRS/University Paul Sabatier, Toulouse, France; ...
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Robust preconditioners via generalized eigenproblems for hybrid sparse linear solvers
... Table 1 A weak scalability study was performed using the MaPHyS parallel solver. The Setup, Solve and Total times are the max among all subdomains, in seconds (s). Each subdomain is associated with one MPI process ...
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