Guided self-scheduling: A practical scheduling scheme for parallel supercomputers
IEEE Transactions on Computers
Applied multivariate statistical analysis
Applied multivariate statistical analysis
A Divide-and-Conquer Algorithm for the Symmetric TridiagonalEigenproblem
SIAM Journal on Matrix Analysis and Applications
Deflation Techniques for an Implicitly Restarted Arnoldi Iteration
SIAM Journal on Matrix Analysis and Applications
Matrix computations (3rd ed.)
ScaLAPACK user's guide
The symmetric eigenvalue problem
The symmetric eigenvalue problem
Unitary Triangularization of a Nonsymmetric Matrix
Journal of the ACM (JACM)
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Eigenvalue computation in the 20th century
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. III: linear algebra
MPI-The Complete Reference, Volume 1: The MPI Core
MPI-The Complete Reference, Volume 1: The MPI Core
Software and the Concurrency Revolution
Queue - Multiprocessors
Handbook for Automatic Computation: Linear Algebra (Grundlehren Der Mathematischen Wissenschaften, Vol 186)
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
IEEE Design & Test
Neural Network Implementation Using CUDA and OpenMP
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Introduction to Concurrency in Programming Languages
Introduction to Concurrency in Programming Languages
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
WSEAS Transactions on Information Science and Applications
Graph Spectra for Complex Networks
Graph Spectra for Complex Networks
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This work presents an object-oriented approach to the concurrent computation of eigenvalues and eigenvectors in real symmetric and Hermitian matrices on present memory shared multicore systems. This can be considered the lower level step in a general framework for dealing with large size eigenproblems, where the matrices are factorized to a small enough size. The results show that the proposed parallelization achieves a good speedup in actual systems with up to four cores. Also, it is observed that the limiting performance factor is the number of threads rather than the size of the matrix. We also find that a reasonable upper limit for a "small" dense matrix to be treated in actual processors is in the interval 10000-30000.