A note on generating finer‐grain parallelism in a representation tree
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Publication:4924936
DOI10.1002/nla.828zbMath1274.65108OpenAlexW1899806734MaRDI QIDQ4924936
Publication date: 10 June 2013
Published in: Numerical Linear Algebra with Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/nla.828
complexityalgorithmparallelismorthogonal eigenvectorssymmetric tridiagonal matrixmulticoreGPUgraphics processing unitLAPACKeigenvalue clustersmultiple relatively robust representationsrepresentation treespectrum peeling
Trees (05C05) Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Graphs and linear algebra (matrices, eigenvalues, etc.) (05C50) Parallel numerical computation (65Y05) Complexity and performance of numerical algorithms (65Y20)
Uses Software
Cites Work
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- Accurate singular values and differential qd algorithms
- Fernando's solution to Wilkinson's problem: An application of double factorization
- Multiple representations to compute orthogonal eigenvectors of symmetric tridiagonal matrices
- Relatively robust representations of symmetric tridiagonals
- An implementation of the dqds algorithm (positive case)
- ScaLAPACK's MRRR algorithm
- Accurate Singular Values of Bidiagonal Matrices
- The design and implementation of the MRRR algorithm
- Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy
- Performance and Accuracy of LAPACK's Symmetric Tridiagonal Eigensolvers
- Computing an Eigenvector with Inverse Iteration
- Orthogonal Eigenvectors and Relative Gaps
- Implicit Cholesky algorithms for singular values and vectors of triangular matrices
- A Bidiagonal Matrix Determines Its Hyperbolic SVD to Varied Relative Accuracy
- Some new bounds on perturbation of subspaces
- The Rotation of Eigenvectors by a Perturbation. III
- A Parallel Eigensolver for Dense Symmetric Matrices Based on Multiple Relatively Robust Representations
