The Laguerre iteration in solving the symmetric tridiagonal eigenproblem, revisited
SIAM Journal on Scientific Computing
A Divide-and-Conquer Algorithm for the Symmetric TridiagonalEigenproblem
SIAM Journal on Matrix Analysis and Applications
A scalable eigenvalue solver for symmetric tridiagonal matrices
Parallel Computing
Applied numerical linear algebra
Applied numerical linear algebra
Toward an Efficient Parallel Eigensolver for Dense Symmetric Matrices
SIAM Journal on Scientific Computing
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Proceedings of the 2000 ACM/IEEE conference on Supercomputing
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SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
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Terascale spectral element dynamical core for atmospheric general circulation models
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
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International Journal of High Performance Computing Applications
Solving the symmetric tridiagonal eigenproblem using MPI/OpenMP hybrid parallelization
APPT'05 Proceedings of the 6th international conference on Advanced Parallel Processing Technologies
Spectral analysis for billion-scale graphs: discoveries and implementation
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
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This paper describes a hybrid two-level parallel method with MPI/OpenMP for computing the eigenvalues of dense symmetric matrices on cluster of SMP's environments. The eigenvalue computation is Based on both the Householder tridiagonalization method and a divide-and-conquer algorithm of tridiagonal eigenproblem. In hybrid parallel design, We take a coarse-grain approach to OpenMP shared-memory parallelization, which keeps BLAS-3 operations in tridiagonalization. Moreover, dynamic work sharing is used in the divide-and-conquer algorithm of tridiagonal eigenproblem. So the amount of synchronization has also been reduced, and these could have an effect on the load balance. In addition, we analyze the communication overhead between hybrid MPI/ OpenMP and pure MPI. An experimental analysis on the Deepcomp6800 shows the hybrid algorithm performs good scalability.