LAPACK 3.12.0
LAPACK: Linear Algebra PACKage

◆ dsb2st_kernels()

subroutine dsb2st_kernels ( character  UPLO,
logical  WANTZ,
integer  TTYPE,
integer  ST,
integer  ED,
integer  SWEEP,
integer  N,
integer  NB,
integer  IB,
double precision, dimension( lda, * )  A,
integer  LDA,
double precision, dimension( * )  V,
double precision, dimension( * )  TAU,
integer  LDVT,
double precision, dimension( * )  WORK 
)

DSB2ST_KERNELS

Download DSB2ST_KERNELS + dependencies [TGZ] [ZIP] [TXT]

Purpose:
 DSB2ST_KERNELS is an internal routine used by the DSYTRD_SB2ST
 subroutine.
Parameters
[in]UPLO
          UPLO is CHARACTER*1
[in]WANTZ
          WANTZ is LOGICAL which indicate if Eigenvalue are requested or both
          Eigenvalue/Eigenvectors.
[in]TTYPE
          TTYPE is INTEGER
[in]ST
          ST is INTEGER
          internal parameter for indices.
[in]ED
          ED is INTEGER
          internal parameter for indices.
[in]SWEEP
          SWEEP is INTEGER
          internal parameter for indices.
[in]N
          N is INTEGER. The order of the matrix A.
[in]NB
          NB is INTEGER. The size of the band.
[in]IB
          IB is INTEGER.
[in,out]A
          A is DOUBLE PRECISION array. A pointer to the matrix A.
[in]LDA
          LDA is INTEGER. The leading dimension of the matrix A.
[out]V
          V is DOUBLE PRECISION array, dimension 2*n if eigenvalues only are
          requested or to be queried for vectors.
[out]TAU
          TAU is DOUBLE PRECISION array, dimension (2*n).
          The scalar factors of the Householder reflectors are stored
          in this array.
[in]LDVT
          LDVT is INTEGER.
[out]WORK
          WORK is DOUBLE PRECISION array. Workspace of size nb.
Further Details:
  Implemented by Azzam Haidar.

  All details are available on technical report, SC11, SC13 papers.

  Azzam Haidar, Hatem Ltaief, and Jack Dongarra.
  Parallel reduction to condensed forms for symmetric eigenvalue problems
  using aggregated fine-grained and memory-aware kernels. In Proceedings
  of 2011 International Conference for High Performance Computing,
  Networking, Storage and Analysis (SC '11), New York, NY, USA,
  Article 8 , 11 pages.
  http://doi.acm.org/10.1145/2063384.2063394

  A. Haidar, J. Kurzak, P. Luszczek, 2013.
  An improved parallel singular value algorithm and its implementation
  for multicore hardware, In Proceedings of 2013 International Conference
  for High Performance Computing, Networking, Storage and Analysis (SC '13).
  Denver, Colorado, USA, 2013.
  Article 90, 12 pages.
  http://doi.acm.org/10.1145/2503210.2503292

  A. Haidar, R. Solca, S. Tomov, T. Schulthess and J. Dongarra.
  A novel hybrid CPU-GPU generalized eigensolver for electronic structure
  calculations based on fine-grained memory aware tasks.
  International Journal of High Performance Computing Applications.
  Volume 28 Issue 2, Pages 196-209, May 2014.
  http://hpc.sagepub.com/content/28/2/196