Enlarged Krylov Subspace Conjugate Gradient Methods for Reducing Communication

DOI10.1137/140989492zbMath1382.65086OpenAlexW9532344MaRDI QIDQ2813336

Laura Grigori, Sophie Moufawad, Frédéric Nataf

Publication date: 23 June 2016

Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/42a165fe6df5fbb86753ae65b24c926a80105ef5

zbMATH Keywords

iterative methodslinear algebraminimizing communication

Mathematics Subject Classification ID

Parallel algorithms in computer science (68W10) Iterative numerical methods for linear systems (65F10)

Related Items (12)

Partitioning and Reordering for Spike-Based Distributed-Memory Parallel Gauss--Seidel ⋮ Limited‐memory polynomial methods for large‐scale matrix functions ⋮ Adaptively restarted block Krylov subspace methods with low-synchronization skeletons ⋮ A Class of Efficient Locally Constructed Preconditioners Based on Coarse Spaces ⋮ Introduction to Communication Avoiding Algorithms for Direct Methods of Factorization in Linear Algebra ⋮ Strategies for the Vectorized Block Conjugate Gradients Method ⋮ s-Step Enlarged Krylov Subspace Conjugate Gradient Methods ⋮ Enlarged Krylov Subspace Conjugate Gradient Methods for Reducing Communication ⋮ The Stability of Block Variants of Classical Gram--Schmidt ⋮ Block Gram-Schmidt algorithms and their stability properties ⋮ Scalable Linear Solvers Based on Enlarged Krylov Subspaces with Dynamic Reduction of Search Directions ⋮ Numerical algorithms for high-performance computational science

Uses Software

• FreeFem++
• METIS

Cites Work

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