In this paper, some parallel algorithms are described for solving numerical linear algebra problems on Dawning-1000. They include matrix multiplication, LU factorization of a dense matrix, Cholesky factorization of a ...In this paper, some parallel algorithms are described for solving numerical linear algebra problems on Dawning-1000. They include matrix multiplication, LU factorization of a dense matrix, Cholesky factorization of a symmetric matrix, and eigendecomposition of symmetric matrix for real and complex data types. These programs are constructed based on fast BLAS library of Dawning-1000 under NX environment.Some comparison results under different parallel environments and implementing methods are also given for Cholesky factorization. The execution time, measured performance and speedup for each problem on Dawning-1000 are shown. For matrix multiplication and LU factorization, 1.86GFLOPS and 1.53GFLOPS are reached.展开更多
Many applications in computational science and engineering require the computation of eigenvalues and vectors of dense symmetric or Hermitian matrices. For example, in DFT （density functional theory） calculations on...Many applications in computational science and engineering require the computation of eigenvalues and vectors of dense symmetric or Hermitian matrices. For example, in DFT （density functional theory） calculations on modern supercomputers 10% to 30% of the eigenvalues and eigenvectors of huge dense matrices have to be calculated. Therefore, performance and parallel scaling of the used eigensolvers is of upmost interest. In this article different routines of the linear algebra packages ScaLAPACK and Elemental for parallel solution of the symmetric eigenvalue problem are compared concerning their performance on the BlueGene/P supercomputer. Parameters for performance optimization are adjusted for the different data distribution methods used in the two libraries. It is found that for all test cases the new library Elemental which uses a two-dimensional element by element distribution of the matrices to the processors shows better performance than the old ScaLAPACK library which uses a block-cyclic distribution.展开更多
Aims and Scope The Journal of Computational Mathematics is published bi-monthly.It is an international journal covering all branches of modern computational mathematics such as numerical linear algebra,numerical optim...Aims and Scope The Journal of Computational Mathematics is published bi-monthly.It is an international journal covering all branches of modern computational mathematics such as numerical linear algebra,numerical optimization,computational geometry,numerical PDEs and inverse problems.Papers containing new ideas,creative approaches and/or innovative applications as well as invited reviews are expected to appear regularly in the journal.展开更多
Aims and Scope The Journal of Computational Mathematics is published bi-monthly.It is an international journal covering all branches of modern computational mathematics such as numerical linear algebra.
文摘In this paper, some parallel algorithms are described for solving numerical linear algebra problems on Dawning-1000. They include matrix multiplication, LU factorization of a dense matrix, Cholesky factorization of a symmetric matrix, and eigendecomposition of symmetric matrix for real and complex data types. These programs are constructed based on fast BLAS library of Dawning-1000 under NX environment.Some comparison results under different parallel environments and implementing methods are also given for Cholesky factorization. The execution time, measured performance and speedup for each problem on Dawning-1000 are shown. For matrix multiplication and LU factorization, 1.86GFLOPS and 1.53GFLOPS are reached.
文摘Many applications in computational science and engineering require the computation of eigenvalues and vectors of dense symmetric or Hermitian matrices. For example, in DFT （density functional theory） calculations on modern supercomputers 10% to 30% of the eigenvalues and eigenvectors of huge dense matrices have to be calculated. Therefore, performance and parallel scaling of the used eigensolvers is of upmost interest. In this article different routines of the linear algebra packages ScaLAPACK and Elemental for parallel solution of the symmetric eigenvalue problem are compared concerning their performance on the BlueGene/P supercomputer. Parameters for performance optimization are adjusted for the different data distribution methods used in the two libraries. It is found that for all test cases the new library Elemental which uses a two-dimensional element by element distribution of the matrices to the processors shows better performance than the old ScaLAPACK library which uses a block-cyclic distribution.
文摘Aims and Scope The Journal of Computational Mathematics is published bi-monthly.It is an international journal covering all branches of modern computational mathematics such as numerical linear algebra,numerical optimization,computational geometry,numerical PDEs and inverse problems.Papers containing new ideas,creative approaches and/or innovative applications as well as invited reviews are expected to appear regularly in the journal.
文摘Aims and Scope The Journal of Computational Mathematics is published bi-monthly.It is an international journal covering all branches of modern computational mathematics such as numerical linear algebra.
