elpa1.F90 129 KB
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!    This file is part of ELPA.
!
!    The ELPA library was originally created by the ELPA consortium, 
!    consisting of the following organizations:
!
!    - Rechenzentrum Garching der Max-Planck-Gesellschaft (RZG), 
!    - Bergische Universität Wuppertal, Lehrstuhl für angewandte
!      Informatik,
!    - Technische Universität München, Lehrstuhl für Informatik mit
!      Schwerpunkt Wissenschaftliches Rechnen , 
!    - Fritz-Haber-Institut, Berlin, Abt. Theorie, 
!    - Max-Plack-Institut für Mathematik in den Naturwissenschaftrn, 
!      Leipzig, Abt. Komplexe Strukutren in Biologie und Kognition, 
!      and  
!    - IBM Deutschland GmbH
!
!
!    More information can be found here:
!    http://elpa.rzg.mpg.de/
!
!    ELPA is free software: you can redistribute it and/or modify
!    it under the terms of the version 3 of the license of the 
!    GNU Lesser General Public License as published by the Free 
!    Software Foundation.
!
!    ELPA is distributed in the hope that it will be useful,
!    but WITHOUT ANY WARRANTY; without even the implied warranty of
!    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
!    GNU Lesser General Public License for more details.
!
!    You should have received a copy of the GNU Lesser General Public License
!    along with ELPA.  If not, see <http://www.gnu.org/licenses/>
!
!    ELPA reflects a substantial effort on the part of the original
!    ELPA consortium, and we ask you to respect the spirit of the
!    license that we chose: i.e., please contribute any changes you
!    may have back to the original ELPA library distribution, and keep
!    any derivatives of ELPA under the same license that we chose for
!    the original distribution, the GNU Lesser General Public License.
!
!
! ELPA1 -- Faster replacements for ScaLAPACK symmetric eigenvalue routines
! 
! Copyright of the original code rests with the authors inside the ELPA
! consortium. The copyright of any additional modifications shall rest
! with their original authors, but shall adhere to the licensing terms
! distributed along with the original code in the file "COPYING".

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#include "config-f90.h"

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module ELPA1

! Version 1.1.2, 2011-02-21

  implicit none

  PRIVATE ! By default, all routines contained are private

  ! The following routines are public:

  public :: get_elpa_row_col_comms     ! Sets MPI row/col communicators

  public :: solve_evp_real             ! Driver routine for real eigenvalue problem
  public :: solve_evp_complex          ! Driver routine for complex eigenvalue problem

  public :: tridiag_real               ! Transform real symmetric matrix to tridiagonal form
  public :: trans_ev_real              ! Transform eigenvectors of a tridiagonal matrix back
  public :: mult_at_b_real             ! Multiply real matrices A**T * B

  public :: tridiag_complex            ! Transform complex hermitian matrix to tridiagonal form
  public :: trans_ev_complex           ! Transform eigenvectors of a tridiagonal matrix back
  public :: mult_ah_b_complex          ! Multiply complex matrices A**H * B

  public :: solve_tridi                ! Solve tridiagonal eigensystem with divide and conquer method

  public :: cholesky_real              ! Cholesky factorization of a real matrix
  public :: invert_trm_real            ! Invert real triangular matrix

  public :: cholesky_complex           ! Cholesky factorization of a complex matrix
  public :: invert_trm_complex         ! Invert complex triangular matrix

  public :: local_index                ! Get local index of a block cyclic distributed matrix
  public :: least_common_multiple      ! Get least common multiple

  public :: hh_transform_real
  public :: hh_transform_complex

!-------------------------------------------------------------------------------

  ! Timing results, set by every call to solve_evp_xxx

  real*8, public :: time_evp_fwd    ! forward transformations (to tridiagonal form)
  real*8, public :: time_evp_solve  ! time for solving the tridiagonal system
  real*8, public :: time_evp_back   ! time for back transformations of eigenvectors

  ! Set elpa_print_times to .true. for explicit timing outputs

  logical, public :: elpa_print_times = .false.

!-------------------------------------------------------------------------------

  include 'mpif.h'

contains

!-------------------------------------------------------------------------------

subroutine get_elpa_row_col_comms(mpi_comm_global, my_prow, my_pcol, mpi_comm_rows, mpi_comm_cols)

!-------------------------------------------------------------------------------
! get_elpa_row_col_comms:
! All ELPA routines need MPI communicators for communicating within
! rows or columns of processes, these are set here.
! mpi_comm_rows/mpi_comm_cols can be free'd with MPI_Comm_free if not used any more.
!
!  Parameters
!
!  mpi_comm_global   Global communicator for the calculations (in)
!
!  my_prow           Row coordinate of the calling process in the process grid (in)
!
!  my_pcol           Column coordinate of the calling process in the process grid (in)
!
!  mpi_comm_rows     Communicator for communicating within rows of processes (out)
!
!  mpi_comm_cols     Communicator for communicating within columns of processes (out)
!
!-------------------------------------------------------------------------------

   implicit none

   integer, intent(in)  :: mpi_comm_global, my_prow, my_pcol
   integer, intent(out) :: mpi_comm_rows, mpi_comm_cols

   integer :: mpierr

   ! mpi_comm_rows is used for communicating WITHIN rows, i.e. all processes
   ! having the same column coordinate share one mpi_comm_rows.
   ! So the "color" for splitting is my_pcol and the "key" is my row coordinate.
   ! Analogous for mpi_comm_cols

   call mpi_comm_split(mpi_comm_global,my_pcol,my_prow,mpi_comm_rows,mpierr)
   call mpi_comm_split(mpi_comm_global,my_prow,my_pcol,mpi_comm_cols,mpierr)

end subroutine get_elpa_row_col_comms

!-------------------------------------------------------------------------------

subroutine solve_evp_real(na, nev, a, lda, ev, q, ldq, nblk, mpi_comm_rows, mpi_comm_cols)

!-------------------------------------------------------------------------------
!  solve_evp_real: Solves the real eigenvalue problem
!
!  Parameters
!
!  na          Order of matrix a
!
!  nev         Number of eigenvalues needed.
!              The smallest nev eigenvalues/eigenvectors are calculated.
!
!  a(lda,*)    Distributed matrix for which eigenvalues are to be computed.
!              Distribution is like in Scalapack.
!              The full matrix must be set (not only one half like in scalapack).
!              Destroyed on exit (upper and lower half).
!
!  lda         Leading dimension of a
!
!  ev(na)      On output: eigenvalues of a, every processor gets the complete set
!
!  q(ldq,*)    On output: Eigenvectors of a
!              Distribution is like in Scalapack.
!              Must be always dimensioned to the full size (corresponding to (na,na))
!              even if only a part of the eigenvalues is needed.
!
!  ldq         Leading dimension of q
!
!  nblk        blocksize of cyclic distribution, must be the same in both directions!
!
!  mpi_comm_rows
!  mpi_comm_cols
!              MPI-Communicators for rows/columns
!
!-------------------------------------------------------------------------------

   implicit none

   integer, intent(in) :: na, nev, lda, ldq, nblk, mpi_comm_rows, mpi_comm_cols
   real*8 :: a(lda,*), ev(na), q(ldq,*)

   integer my_prow, my_pcol, mpierr
   real*8, allocatable :: e(:), tau(:)
   real*8 ttt0, ttt1

   call mpi_comm_rank(mpi_comm_rows,my_prow,mpierr)
   call mpi_comm_rank(mpi_comm_cols,my_pcol,mpierr)

   allocate(e(na), tau(na))

   ttt0 = MPI_Wtime()
   call tridiag_real(na, a, lda, nblk, mpi_comm_rows, mpi_comm_cols, ev, e, tau)
   ttt1 = MPI_Wtime()
   if(my_prow==0 .and. my_pcol==0 .and. elpa_print_times) print *,'Time tridiag_real :',ttt1-ttt0
   time_evp_fwd = ttt1-ttt0

   ttt0 = MPI_Wtime()
   call solve_tridi(na, nev, ev, e, q, ldq, nblk, mpi_comm_rows, mpi_comm_cols)
   ttt1 = MPI_Wtime()
   if(my_prow==0 .and. my_pcol==0 .and. elpa_print_times) print *,'Time solve_tridi  :',ttt1-ttt0
   time_evp_solve = ttt1-ttt0

   ttt0 = MPI_Wtime()
   call trans_ev_real(na, nev, a, lda, tau, q, ldq, nblk, mpi_comm_rows, mpi_comm_cols)
   ttt1 = MPI_Wtime()
   if(my_prow==0 .and. my_pcol==0 .and. elpa_print_times) print *,'Time trans_ev_real:',ttt1-ttt0
   time_evp_back = ttt1-ttt0

   deallocate(e, tau)

end subroutine solve_evp_real

!-------------------------------------------------------------------------------


subroutine solve_evp_complex(na, nev, a, lda, ev, q, ldq, nblk, mpi_comm_rows, mpi_comm_cols)

!-------------------------------------------------------------------------------
!  solve_evp_complex: Solves the complex eigenvalue problem
!
!  Parameters
!
!  na          Order of matrix a
!
!  nev         Number of eigenvalues needed
!              The smallest nev eigenvalues/eigenvectors are calculated.
!
!  a(lda,*)    Distributed matrix for which eigenvalues are to be computed.
!              Distribution is like in Scalapack.
!              The full matrix must be set (not only one half like in scalapack).
!              Destroyed on exit (upper and lower half).
!
!  lda         Leading dimension of a
!
!  ev(na)      On output: eigenvalues of a, every processor gets the complete set
!
!  q(ldq,*)    On output: Eigenvectors of a
!              Distribution is like in Scalapack.
!              Must be always dimensioned to the full size (corresponding to (na,na))
!              even if only a part of the eigenvalues is needed.
!
!  ldq         Leading dimension of q
!
!  nblk        blocksize of cyclic distribution, must be the same in both directions!
!
!  mpi_comm_rows
!  mpi_comm_cols
!              MPI-Communicators for rows/columns
!
!-------------------------------------------------------------------------------

   implicit none

   integer, intent(in) :: na, nev, lda, ldq, nblk, mpi_comm_rows, mpi_comm_cols
   complex*16 :: a(lda,*), q(ldq,*)
   real*8 :: ev(na)

   integer my_prow, my_pcol, np_rows, np_cols, mpierr
   integer l_rows, l_cols, l_cols_nev
   real*8, allocatable :: q_real(:,:), e(:)
   complex*16, allocatable :: tau(:)
   real*8 ttt0, ttt1

   call mpi_comm_rank(mpi_comm_rows,my_prow,mpierr)
   call mpi_comm_size(mpi_comm_rows,np_rows,mpierr)
   call mpi_comm_rank(mpi_comm_cols,my_pcol,mpierr)
   call mpi_comm_size(mpi_comm_cols,np_cols,mpierr)

   l_rows = local_index(na, my_prow, np_rows, nblk, -1) ! Local rows of a and q
   l_cols = local_index(na, my_pcol, np_cols, nblk, -1) ! Local columns of q

   l_cols_nev = local_index(nev, my_pcol, np_cols, nblk, -1) ! Local columns corresponding to nev

   allocate(e(na), tau(na))
   allocate(q_real(l_rows,l_cols))

   ttt0 = MPI_Wtime()
   call tridiag_complex(na, a, lda, nblk, mpi_comm_rows, mpi_comm_cols, ev, e, tau)
   ttt1 = MPI_Wtime()
   if(my_prow==0 .and. my_pcol==0 .and. elpa_print_times) print *,'Time tridiag_complex :',ttt1-ttt0
   time_evp_fwd = ttt1-ttt0

   ttt0 = MPI_Wtime()
   call solve_tridi(na, nev, ev, e, q_real, l_rows, nblk, mpi_comm_rows, mpi_comm_cols)
   ttt1 = MPI_Wtime()
   if(my_prow==0 .and. my_pcol==0 .and. elpa_print_times) print *,'Time solve_tridi     :',ttt1-ttt0
   time_evp_solve = ttt1-ttt0

   ttt0 = MPI_Wtime()
   q(1:l_rows,1:l_cols_nev) = q_real(1:l_rows,1:l_cols_nev)

   call trans_ev_complex(na, nev, a, lda, tau, q, ldq, nblk, mpi_comm_rows, mpi_comm_cols)
   ttt1 = MPI_Wtime()
   if(my_prow==0 .and. my_pcol==0 .and. elpa_print_times) print *,'Time trans_ev_complex:',ttt1-ttt0
   time_evp_back = ttt1-ttt0

   deallocate(q_real)
   deallocate(e, tau)

end subroutine solve_evp_complex

!-------------------------------------------------------------------------------

subroutine tridiag_real(na, a, lda, nblk, mpi_comm_rows, mpi_comm_cols, d, e, tau)

!-------------------------------------------------------------------------------
!  tridiag_real: Reduces a distributed symmetric matrix to tridiagonal form
!                (like Scalapack Routine PDSYTRD)
!
!  Parameters
!
!  na          Order of matrix
!
!  a(lda,*)    Distributed matrix which should be reduced.
!              Distribution is like in Scalapack.
!              Opposed to PDSYTRD, a(:,:) must be set completely (upper and lower half)
!              a(:,:) is overwritten on exit with the Householder vectors
!
!  lda         Leading dimension of a
!
!  nblk        blocksize of cyclic distribution, must be the same in both directions!
!
!  mpi_comm_rows
!  mpi_comm_cols
!              MPI-Communicators for rows/columns
!
!  d(na)       Diagonal elements (returned), identical on all processors
!
!  e(na)       Off-Diagonal elements (returned), identical on all processors
!
!  tau(na)     Factors for the Householder vectors (returned), needed for back transformation
!
!-------------------------------------------------------------------------------

   implicit none

   integer na, lda, nblk, mpi_comm_rows, mpi_comm_cols
   real*8 a(lda,*), d(na), e(na), tau(na)

   integer, parameter :: max_stored_rows = 32

   integer my_prow, my_pcol, np_rows, np_cols, mpierr
   integer totalblocks, max_blocks_row, max_blocks_col, max_local_rows, max_local_cols
   integer l_cols, l_rows, nstor
   integer istep, i, j, lcs, lce, lrs, lre
   integer tile_size, l_rows_tile, l_cols_tile

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#ifdef WITH_OPENMP
   integer my_thread, n_threads, max_threads, n_iter
   integer omp_get_thread_num, omp_get_num_threads, omp_get_max_threads
#endif

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   real*8 vav, vnorm2, x, aux(2*max_stored_rows), aux1(2), aux2(2), vrl, xf

   real*8, allocatable:: tmp(:), vr(:), vc(:), ur(:), uc(:), vur(:,:), uvc(:,:)
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#ifdef WITH_OPENMP
   real*8, allocatable:: ur_p(:,:), uc_p(:,:)
#endif
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   integer pcol, prow
   pcol(i) = MOD((i-1)/nblk,np_cols) !Processor col for global col number
   prow(i) = MOD((i-1)/nblk,np_rows) !Processor row for global row number


   call mpi_comm_rank(mpi_comm_rows,my_prow,mpierr)
   call mpi_comm_size(mpi_comm_rows,np_rows,mpierr)
   call mpi_comm_rank(mpi_comm_cols,my_pcol,mpierr)
   call mpi_comm_size(mpi_comm_cols,np_cols,mpierr)

   ! Matrix is split into tiles; work is done only for tiles on the diagonal or above

   tile_size = nblk*least_common_multiple(np_rows,np_cols) ! minimum global tile size
   tile_size = ((128*max(np_rows,np_cols)-1)/tile_size+1)*tile_size ! make local tiles at least 128 wide

   l_rows_tile = tile_size/np_rows ! local rows of a tile
   l_cols_tile = tile_size/np_cols ! local cols of a tile


   totalblocks = (na-1)/nblk + 1
   max_blocks_row = (totalblocks-1)/np_rows + 1
   max_blocks_col = (totalblocks-1)/np_cols + 1

   max_local_rows = max_blocks_row*nblk
   max_local_cols = max_blocks_col*nblk

   allocate(tmp(MAX(max_local_rows,max_local_cols)))
   allocate(vr(max_local_rows+1))
   allocate(ur(max_local_rows))
   allocate(vc(max_local_cols))
   allocate(uc(max_local_cols))

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#ifdef WITH_OPENMP
   max_threads = omp_get_max_threads()

   allocate(ur_p(max_local_rows,0:max_threads-1))
   allocate(uc_p(max_local_cols,0:max_threads-1))
#endif

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   tmp = 0
   vr = 0
   ur = 0
   vc = 0
   uc = 0

   allocate(vur(max_local_rows,2*max_stored_rows))
   allocate(uvc(max_local_cols,2*max_stored_rows))

   d(:) = 0
   e(:) = 0
   tau(:) = 0

   nstor = 0

   l_rows = local_index(na, my_prow, np_rows, nblk, -1) ! Local rows of a
   l_cols = local_index(na, my_pcol, np_cols, nblk, -1) ! Local cols of a
   if(my_prow==prow(na) .and. my_pcol==pcol(na)) d(na) = a(l_rows,l_cols)

   do istep=na,3,-1

      ! Calculate number of local rows and columns of the still remaining matrix
      ! on the local processor

      l_rows = local_index(istep-1, my_prow, np_rows, nblk, -1)
      l_cols = local_index(istep-1, my_pcol, np_cols, nblk, -1)

      ! Calculate vector for Householder transformation on all procs
      ! owning column istep

      if(my_pcol==pcol(istep)) then

         ! Get vector to be transformed; distribute last element and norm of
         ! remaining elements to all procs in current column

         vr(1:l_rows) = a(1:l_rows,l_cols+1)
         if(nstor>0 .and. l_rows>0) then
            call DGEMV('N',l_rows,2*nstor,1.d0,vur,ubound(vur,1), &
                       uvc(l_cols+1,1),ubound(uvc,1),1.d0,vr,1)
         endif

         if(my_prow==prow(istep-1)) then
            aux1(1) = dot_product(vr(1:l_rows-1),vr(1:l_rows-1))
            aux1(2) = vr(l_rows)
         else
            aux1(1) = dot_product(vr(1:l_rows),vr(1:l_rows))
            aux1(2) = 0.
         endif

         call mpi_allreduce(aux1,aux2,2,MPI_REAL8,MPI_SUM,mpi_comm_rows,mpierr)

         vnorm2 = aux2(1)
         vrl    = aux2(2)

         ! Householder transformation

         call hh_transform_real(vrl, vnorm2, xf, tau(istep))

         ! Scale vr and store Householder vector for back transformation

         vr(1:l_rows) = vr(1:l_rows) * xf
         if(my_prow==prow(istep-1)) then
            vr(l_rows) = 1.
            e(istep-1) = vrl
         endif
         a(1:l_rows,l_cols+1) = vr(1:l_rows) ! store Householder vector for back transformation

      endif

      ! Broadcast the Householder vector (and tau) along columns

      if(my_pcol==pcol(istep)) vr(l_rows+1) = tau(istep)
      call MPI_Bcast(vr,l_rows+1,MPI_REAL8,pcol(istep),mpi_comm_cols,mpierr)
      tau(istep) =  vr(l_rows+1)

      ! Transpose Householder vector vr -> vc

      call elpa_transpose_vectors  (vr, ubound(vr,1), mpi_comm_rows, &
                                    vc, ubound(vc,1), mpi_comm_cols, &
                                    1, istep-1, 1, nblk)


      ! Calculate u = (A + VU**T + UV**T)*v

      ! For cache efficiency, we use only the upper half of the matrix tiles for this,
      ! thus the result is partly in uc(:) and partly in ur(:)

      uc(1:l_cols) = 0
      ur(1:l_rows) = 0
      if(l_rows>0 .and. l_cols>0) then

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#ifdef WITH_OPENMP
!$OMP PARALLEL PRIVATE(my_thread,n_threads,n_iter,i,lcs,lce,j,lrs,lre)

         my_thread = omp_get_thread_num()
         n_threads = omp_get_num_threads()

         n_iter = 0

         uc_p(1:l_cols,my_thread) = 0.
         ur_p(1:l_rows,my_thread) = 0.
#endif
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         do i=0,(istep-2)/tile_size
            lcs = i*l_cols_tile+1
            lce = min(l_cols,(i+1)*l_cols_tile)
            if(lce<lcs) cycle
            do j=0,i
               lrs = j*l_rows_tile+1
               lre = min(l_rows,(j+1)*l_rows_tile)
               if(lre<lrs) cycle
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#ifdef WITH_OPENMP
               if(mod(n_iter,n_threads) == my_thread) then
                 call DGEMV('T',lre-lrs+1,lce-lcs+1,1.d0,a(lrs,lcs),lda,vr(lrs),1,1.d0,uc_p(lcs,my_thread),1)
                 if(i/=j) call DGEMV('N',lre-lrs+1,lce-lcs+1,1.d0,a(lrs,lcs),lda,vc(lcs),1,1.d0,ur_p(lrs,my_thread),1)
               endif
               n_iter = n_iter+1
#else
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               call DGEMV('T',lre-lrs+1,lce-lcs+1,1.d0,a(lrs,lcs),lda,vr(lrs),1,1.d0,uc(lcs),1)
               if(i/=j) call DGEMV('N',lre-lrs+1,lce-lcs+1,1.d0,a(lrs,lcs),lda,vc(lcs),1,1.d0,ur(lrs),1)
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#endif
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            enddo
         enddo
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#ifdef WITH_OPENMP
!$OMP END PARALLEL
      
         do i=0,max_threads-1
            uc(1:l_cols) = uc(1:l_cols) + uc_p(1:l_cols,i)
            ur(1:l_rows) = ur(1:l_rows) + ur_p(1:l_rows,i)
         enddo
#endif
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         if(nstor>0) then
            call DGEMV('T',l_rows,2*nstor,1.d0,vur,ubound(vur,1),vr,1,0.d0,aux,1)
            call DGEMV('N',l_cols,2*nstor,1.d0,uvc,ubound(uvc,1),aux,1,1.d0,uc,1)
         endif

      endif

      ! Sum up all ur(:) parts along rows and add them to the uc(:) parts
      ! on the processors containing the diagonal
      ! This is only necessary if ur has been calculated, i.e. if the
      ! global tile size is smaller than the global remaining matrix

      if(tile_size < istep-1) then
         call elpa_reduce_add_vectors  (ur, ubound(ur,1), mpi_comm_rows, &
                                        uc, ubound(uc,1), mpi_comm_cols, &
                                        istep-1, 1, nblk)
      endif

      ! Sum up all the uc(:) parts, transpose uc -> ur

      if(l_cols>0) then
         tmp(1:l_cols) = uc(1:l_cols)
         call mpi_allreduce(tmp,uc,l_cols,MPI_REAL8,MPI_SUM,mpi_comm_rows,mpierr)
      endif

      call elpa_transpose_vectors  (uc, ubound(uc,1), mpi_comm_cols, &
                                    ur, ubound(ur,1), mpi_comm_rows, &
                                    1, istep-1, 1, nblk)

      ! calculate u**T * v (same as v**T * (A + VU**T + UV**T) * v )

      x = 0
      if(l_cols>0) x = dot_product(vc(1:l_cols),uc(1:l_cols))
      call mpi_allreduce(x,vav,1,MPI_REAL8,MPI_SUM,mpi_comm_cols,mpierr)

      ! store u and v in the matrices U and V
      ! these matrices are stored combined in one here

      do j=1,l_rows
         vur(j,2*nstor+1) = tau(istep)*vr(j)
         vur(j,2*nstor+2) = 0.5*tau(istep)*vav*vr(j) - ur(j)
      enddo
      do j=1,l_cols
         uvc(j,2*nstor+1) = 0.5*tau(istep)*vav*vc(j) - uc(j)
         uvc(j,2*nstor+2) = tau(istep)*vc(j)
      enddo

      nstor = nstor+1

      ! If the limit of max_stored_rows is reached, calculate A + VU**T + UV**T

      if(nstor==max_stored_rows .or. istep==3) then

         do i=0,(istep-2)/tile_size
            lcs = i*l_cols_tile+1
            lce = min(l_cols,(i+1)*l_cols_tile)
            lrs = 1
            lre = min(l_rows,(i+1)*l_rows_tile)
            if(lce<lcs .or. lre<lrs) cycle
            call dgemm('N','T',lre-lrs+1,lce-lcs+1,2*nstor,1.d0, &
                       vur(lrs,1),ubound(vur,1),uvc(lcs,1),ubound(uvc,1), &
                       1.d0,a(lrs,lcs),lda)
         enddo

         nstor = 0

      endif

      if(my_prow==prow(istep-1) .and. my_pcol==pcol(istep-1)) then
         if(nstor>0) a(l_rows,l_cols) = a(l_rows,l_cols) &
                        + dot_product(vur(l_rows,1:2*nstor),uvc(l_cols,1:2*nstor))
         d(istep-1) = a(l_rows,l_cols)
      endif

   enddo

   ! Store e(1) and d(1)

   if(my_prow==prow(1) .and. my_pcol==pcol(2)) e(1) = a(1,l_cols) ! use last l_cols value of loop above
   if(my_prow==prow(1) .and. my_pcol==pcol(1)) d(1) = a(1,1)

   deallocate(tmp, vr, ur, vc, uc, vur, uvc)

   ! distribute the arrays d and e to all processors

   allocate(tmp(na))
   tmp = d
   call mpi_allreduce(tmp,d,na,MPI_REAL8,MPI_SUM,mpi_comm_rows,mpierr)
   tmp = d
   call mpi_allreduce(tmp,d,na,MPI_REAL8,MPI_SUM,mpi_comm_cols,mpierr)
   tmp = e
   call mpi_allreduce(tmp,e,na,MPI_REAL8,MPI_SUM,mpi_comm_rows,mpierr)
   tmp = e
   call mpi_allreduce(tmp,e,na,MPI_REAL8,MPI_SUM,mpi_comm_cols,mpierr)
   deallocate(tmp)

end subroutine tridiag_real

!-------------------------------------------------------------------------------

subroutine trans_ev_real(na, nqc, a, lda, tau, q, ldq, nblk, mpi_comm_rows, mpi_comm_cols)

!-------------------------------------------------------------------------------
!  trans_ev_real: Transforms the eigenvectors of a tridiagonal matrix back
!                 to the eigenvectors of the original matrix
!                 (like Scalapack Routine PDORMTR)
!
!  Parameters
!
!  na          Order of matrix a, number of rows of matrix q
!
!  nqc         Number of columns of matrix q
!
!  a(lda,*)    Matrix containing the Householder vectors (i.e. matrix a after tridiag_real)
!              Distribution is like in Scalapack.
!
!  lda         Leading dimension of a
!
!  tau(na)     Factors of the Householder vectors
!
!  q           On input: Eigenvectors of tridiagonal matrix
!              On output: Transformed eigenvectors
!              Distribution is like in Scalapack.
!
!  ldq         Leading dimension of q
!
!  nblk        blocksize of cyclic distribution, must be the same in both directions!
!
!  mpi_comm_rows
!  mpi_comm_cols
!              MPI-Communicators for rows/columns
!
!-------------------------------------------------------------------------------

   implicit none

   integer na, nqc, lda, ldq, nblk, mpi_comm_rows, mpi_comm_cols
   real*8 a(lda,*), q(ldq,*), tau(na)

   integer :: max_stored_rows

   integer my_prow, my_pcol, np_rows, np_cols, mpierr
   integer totalblocks, max_blocks_row, max_blocks_col, max_local_rows, max_local_cols
   integer l_cols, l_rows, l_colh, nstor
   integer istep, i, n, nc, ic, ics, ice, nb, cur_pcol

   real*8, allocatable:: tmp1(:), tmp2(:), hvb(:), hvm(:,:)
   real*8, allocatable:: tmat(:,:), h1(:), h2(:)

   integer pcol, prow
   pcol(i) = MOD((i-1)/nblk,np_cols) !Processor col for global col number
   prow(i) = MOD((i-1)/nblk,np_rows) !Processor row for global row number


   call mpi_comm_rank(mpi_comm_rows,my_prow,mpierr)
   call mpi_comm_size(mpi_comm_rows,np_rows,mpierr)
   call mpi_comm_rank(mpi_comm_cols,my_pcol,mpierr)
   call mpi_comm_size(mpi_comm_cols,np_cols,mpierr)


   totalblocks = (na-1)/nblk + 1
   max_blocks_row = (totalblocks-1)/np_rows + 1
   max_blocks_col = ((nqc-1)/nblk)/np_cols + 1  ! Columns of q!

   max_local_rows = max_blocks_row*nblk
   max_local_cols = max_blocks_col*nblk


   max_stored_rows = (63/nblk+1)*nblk

   allocate(tmat(max_stored_rows,max_stored_rows))
   allocate(h1(max_stored_rows*max_stored_rows))
   allocate(h2(max_stored_rows*max_stored_rows))
   allocate(tmp1(max_local_cols*max_stored_rows))
   allocate(tmp2(max_local_cols*max_stored_rows))
   allocate(hvb(max_local_rows*nblk))
   allocate(hvm(max_local_rows,max_stored_rows))

   hvm = 0   ! Must be set to 0 !!!
   hvb = 0   ! Safety only

   l_cols = local_index(nqc, my_pcol, np_cols, nblk, -1) ! Local columns of q

   nstor = 0

   do istep=1,na,nblk

      ics = MAX(istep,3)
      ice = MIN(istep+nblk-1,na)
      if(ice<ics) cycle

      cur_pcol = pcol(istep)

      nb = 0
      do ic=ics,ice

         l_colh = local_index(ic  , my_pcol, np_cols, nblk, -1) ! Column of Householder vector
         l_rows = local_index(ic-1, my_prow, np_rows, nblk, -1) ! # rows of Householder vector


         if(my_pcol==cur_pcol) then
            hvb(nb+1:nb+l_rows) = a(1:l_rows,l_colh)
            if(my_prow==prow(ic-1)) then
               hvb(nb+l_rows) = 1.
            endif
         endif

         nb = nb+l_rows
      enddo

      if(nb>0) &
         call MPI_Bcast(hvb,nb,MPI_REAL8,cur_pcol,mpi_comm_cols,mpierr)

      nb = 0
      do ic=ics,ice
         l_rows = local_index(ic-1, my_prow, np_rows, nblk, -1) ! # rows of Householder vector
         hvm(1:l_rows,nstor+1) = hvb(nb+1:nb+l_rows)
         nstor = nstor+1
         nb = nb+l_rows
      enddo

      ! Please note: for smaller matix sizes (na/np_rows<=256), a value of 32 for nstor is enough!
      if(nstor+nblk>max_stored_rows .or. istep+nblk>na .or. (na/np_rows<=256 .and. nstor>=32)) then

         ! Calculate scalar products of stored vectors.
         ! This can be done in different ways, we use dsyrk

         tmat = 0
         if(l_rows>0) &
            call dsyrk('U','T',nstor,l_rows,1.d0,hvm,ubound(hvm,1),0.d0,tmat,max_stored_rows)

         nc = 0
         do n=1,nstor-1
            h1(nc+1:nc+n) = tmat(1:n,n+1)
            nc = nc+n
         enddo

         if(nc>0) call mpi_allreduce(h1,h2,nc,MPI_REAL8,MPI_SUM,mpi_comm_rows,mpierr)

         ! Calculate triangular matrix T

         nc = 0
         tmat(1,1) = tau(ice-nstor+1)
         do n=1,nstor-1
            call dtrmv('L','T','N',n,tmat,max_stored_rows,h2(nc+1),1)
            tmat(n+1,1:n) = -h2(nc+1:nc+n)*tau(ice-nstor+n+1)
            tmat(n+1,n+1) = tau(ice-nstor+n+1)
            nc = nc+n
         enddo

         ! Q = Q - V * T * V**T * Q

         if(l_rows>0) then
            call dgemm('T','N',nstor,l_cols,l_rows,1.d0,hvm,ubound(hvm,1), &
                       q,ldq,0.d0,tmp1,nstor)
         else
            tmp1(1:l_cols*nstor) = 0
         endif
         call mpi_allreduce(tmp1,tmp2,nstor*l_cols,MPI_REAL8,MPI_SUM,mpi_comm_rows,mpierr)
         if(l_rows>0) then
            call dtrmm('L','L','N','N',nstor,l_cols,1.0d0,tmat,max_stored_rows,tmp2,nstor)
            call dgemm('N','N',l_rows,l_cols,nstor,-1.d0,hvm,ubound(hvm,1), &
                       tmp2,nstor,1.d0,q,ldq)
         endif
         nstor = 0
      endif

   enddo

   deallocate(tmat, h1, h2, tmp1, tmp2, hvb, hvm)


end subroutine trans_ev_real

!-------------------------------------------------------------------------------

subroutine mult_at_b_real(uplo_a, uplo_c, na, ncb, a, lda, b, ldb, nblk, mpi_comm_rows, mpi_comm_cols, c, ldc)

!-------------------------------------------------------------------------------
!  mult_at_b_real:  Performs C := A**T * B
!
!      where:  A is a square matrix (na,na) which is optionally upper or lower triangular
!              B is a (na,ncb) matrix
!              C is a (na,ncb) matrix where optionally only the upper or lower
!              triangle may be computed
!
!  Parameters
!
!  uplo_a      'U' if A is upper triangular
!              'L' if A is lower triangular
!              anything else if A is a full matrix
!              Please note: This pertains to the original A (as set in the calling program)
!              whereas the transpose of A is used for calculations
!              If uplo_a is 'U' or 'L', the other triangle is not used at all,
!              i.e. it may contain arbitrary numbers
!
!  uplo_c      'U' if only the upper diagonal part of C is needed
!              'L' if only the upper diagonal part of C is needed
!              anything else if the full matrix C is needed
!              Please note: Even when uplo_c is 'U' or 'L', the other triangle may be
!              written to a certain extent, i.e. one shouldn't rely on the content there!
!
!  na          Number of rows/columns of A, number of rows of B and C
!
!  ncb         Number of columns  of B and C
!
!  a           Matrix A
!
!  lda         Leading dimension of a
!
!  b           Matrix B
!
!  ldb         Leading dimension of b
!
!  nblk        blocksize of cyclic distribution, must be the same in both directions!
!
!  mpi_comm_rows
!  mpi_comm_cols
!              MPI-Communicators for rows/columns
!
!  c           Matrix C
!
!  ldc         Leading dimension of c
!
!-------------------------------------------------------------------------------

   implicit none

   character*1 uplo_a, uplo_c

   integer na, ncb, lda, ldb, nblk, mpi_comm_rows, mpi_comm_cols, ldc
   real*8 a(lda,*), b(ldb,*), c(ldc,*)

   integer my_prow, my_pcol, np_rows, np_cols, mpierr
   integer l_cols, l_rows, l_rows_np
   integer np, n, nb, nblk_mult, lrs, lre, lcs, lce
   integer gcol_min, gcol, goff
   integer nstor, nr_done, noff, np_bc, n_aux_bc, nvals
   integer, allocatable :: lrs_save(:), lre_save(:)

   logical a_lower, a_upper, c_lower, c_upper

   real*8, allocatable:: aux_mat(:,:), aux_bc(:), tmp1(:,:), tmp2(:,:)


   call mpi_comm_rank(mpi_comm_rows,my_prow,mpierr)
   call mpi_comm_size(mpi_comm_rows,np_rows,mpierr)
   call mpi_comm_rank(mpi_comm_cols,my_pcol,mpierr)
   call mpi_comm_size(mpi_comm_cols,np_cols,mpierr)

   l_rows = local_index(na,  my_prow, np_rows, nblk, -1) ! Local rows of a and b
   l_cols = local_index(ncb, my_pcol, np_cols, nblk, -1) ! Local cols of b

   ! Block factor for matrix multiplications, must be a multiple of nblk

   if(na/np_rows<=256) then
      nblk_mult = (31/nblk+1)*nblk
   else
      nblk_mult = (63/nblk+1)*nblk
   endif

   allocate(aux_mat(l_rows,nblk_mult))
   allocate(aux_bc(l_rows*nblk))
   allocate(lrs_save(nblk))
   allocate(lre_save(nblk))

   a_lower = .false.
   a_upper = .false.
   c_lower = .false.
   c_upper = .false.

   if(uplo_a=='u' .or. uplo_a=='U') a_upper = .true.
   if(uplo_a=='l' .or. uplo_a=='L') a_lower = .true.
   if(uplo_c=='u' .or. uplo_c=='U') c_upper = .true.
   if(uplo_c=='l' .or. uplo_c=='L') c_lower = .true.

   ! Build up the result matrix by processor rows

   do np = 0, np_rows-1

      ! In this turn, procs of row np assemble the result

      l_rows_np = local_index(na, np, np_rows, nblk, -1) ! local rows on receiving processors

      nr_done = 0 ! Number of rows done
      aux_mat = 0
      nstor = 0   ! Number of columns stored in aux_mat

      ! Loop over the blocks on row np

      do nb=0,(l_rows_np-1)/nblk

         goff  = nb*np_rows + np ! Global offset in blocks corresponding to nb

         ! Get the processor column which owns this block (A is transposed, so we need the column)
         ! and the offset in blocks within this column.
         ! The corresponding block column in A is then broadcast to all for multiplication with B

         np_bc = MOD(goff,np_cols)
         noff = goff/np_cols
         n_aux_bc = 0

         ! Gather up the complete block column of A on the owner

         do n = 1, min(l_rows_np-nb*nblk,nblk) ! Loop over columns to be broadcast

            gcol = goff*nblk + n ! global column corresponding to n
            if(nstor==0 .and. n==1) gcol_min = gcol

            lrs = 1       ! 1st local row number for broadcast
            lre = l_rows  ! last local row number for broadcast
            if(a_lower) lrs = local_index(gcol, my_prow, np_rows, nblk, +1)
            if(a_upper) lre = local_index(gcol, my_prow, np_rows, nblk, -1)

            if(lrs<=lre) then
               nvals = lre-lrs+1
               if(my_pcol == np_bc) aux_bc(n_aux_bc+1:n_aux_bc+nvals) = a(lrs:lre,noff*nblk+n)
               n_aux_bc = n_aux_bc + nvals
            endif

            lrs_save(n) = lrs
            lre_save(n) = lre

         enddo

         ! Broadcast block column

         call MPI_Bcast(aux_bc,n_aux_bc,MPI_REAL8,np_bc,mpi_comm_cols,mpierr)

         ! Insert what we got in aux_mat

         n_aux_bc = 0
         do n = 1, min(l_rows_np-nb*nblk,nblk)
            nstor = nstor+1
            lrs = lrs_save(n)
            lre = lre_save(n)
            if(lrs<=lre) then
               nvals = lre-lrs+1
               aux_mat(lrs:lre,nstor) = aux_bc(n_aux_bc+1:n_aux_bc+nvals)
               n_aux_bc = n_aux_bc + nvals
            endif
         enddo

         ! If we got nblk_mult columns in aux_mat or this is the last block
         ! do the matrix multiplication

         if(nstor==nblk_mult .or. nb*nblk+nblk >= l_rows_np) then

            lrs = 1       ! 1st local row number for multiply
            lre = l_rows  ! last local row number for multiply
            if(a_lower) lrs = local_index(gcol_min, my_prow, np_rows, nblk, +1)
            if(a_upper) lre = local_index(gcol, my_prow, np_rows, nblk, -1)

            lcs = 1       ! 1st local col number for multiply
            lce = l_cols  ! last local col number for multiply
            if(c_upper) lcs = local_index(gcol_min, my_pcol, np_cols, nblk, +1)
            if(c_lower) lce = MIN(local_index(gcol, my_pcol, np_cols, nblk, -1),l_cols)

            if(lcs<=lce) then
               allocate(tmp1(nstor,lcs:lce),tmp2(nstor,lcs:lce))
               if(lrs<=lre) then
                  call dgemm('T','N',nstor,lce-lcs+1,lre-lrs+1,1.d0,aux_mat(lrs,1),ubound(aux_mat,1), &
                             b(lrs,lcs),ldb,0.d0,tmp1,nstor)
               else
                  tmp1 = 0
               endif

               ! Sum up the results and send to processor row np
               call mpi_reduce(tmp1,tmp2,nstor*(lce-lcs+1),MPI_REAL8,MPI_SUM,np,mpi_comm_rows,mpierr)

               ! Put the result into C
               if(my_prow==np) c(nr_done+1:nr_done+nstor,lcs:lce) = tmp2(1:nstor,lcs:lce)

               deallocate(tmp1,tmp2)
            endif

            nr_done = nr_done+nstor
            nstor=0
            aux_mat(:,:)=0
         endif
      enddo
   enddo

   deallocate(aux_mat, aux_bc, lrs_save, lre_save)

end subroutine mult_at_b_real

!-------------------------------------------------------------------------------

subroutine tridiag_complex(na, a, lda, nblk, mpi_comm_rows, mpi_comm_cols, d, e, tau)

!-------------------------------------------------------------------------------
!  tridiag_complex: Reduces a distributed hermitian matrix to tridiagonal form
!                   (like Scalapack Routine PZHETRD)
!
!  Parameters
!
!  na          Order of matrix
!
!  a(lda,*)    Distributed matrix which should be reduced.
!              Distribution is like in Scalapack.
!              Opposed to PZHETRD, a(:,:) must be set completely (upper and lower half)
!              a(:,:) is overwritten on exit with the Householder vectors
!
!  lda         Leading dimension of a
!
!  nblk        blocksize of cyclic distribution, must be the same in both directions!
!
!  mpi_comm_rows
!  mpi_comm_cols
!              MPI-Communicators for rows/columns
!
!  d(na)       Diagonal elements (returned), identical on all processors
!
!  e(na)       Off-Diagonal elements (returned), identical on all processors
!
!  tau(na)     Factors for the Householder vectors (returned), needed for back transformation
!
!-------------------------------------------------------------------------------

   implicit none

   integer na, lda, nblk, mpi_comm_rows, mpi_comm_cols
   complex*16 a(lda,*), tau(na)
   real*8 d(na), e(na)

   integer, parameter :: max_stored_rows = 32

   complex*16, parameter :: CZERO = (0.d0,0.d0), CONE = (1.d0,0.d0)

   integer my_prow, my_pcol, np_rows, np_cols, mpierr
   integer totalblocks, max_blocks_row, max_blocks_col, max_local_rows, max_local_cols
   integer l_cols, l_rows, nstor
   integer istep, i, j, lcs, lce, lrs, lre
   integer tile_size, l_rows_tile, l_cols_tile

1073 1074 1075 1076 1077
#ifdef WITH_OPENMP
   integer my_thread, n_threads, max_threads, n_iter
   integer omp_get_thread_num, omp_get_num_threads, omp_get_max_threads
#endif

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   real*8 vnorm2
   complex*16 vav, xc, aux(2*max_stored_rows),  aux1(2), aux2(2), vrl, xf

   complex*16, allocatable:: tmp(:), vr(:), vc(:), ur(:), uc(:), vur(:,:), uvc(:,:)
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#ifdef WITH_OPENMP
   complex*16, allocatable:: ur_p(:,:), uc_p(:,:)
#endif
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   real*8, allocatable:: tmpr(:)

   integer pcol, prow
   pcol(i) = MOD((i-1)/nblk,np_cols) !Processor col for global col number
   prow(i) = MOD((i-1)/nblk,np_rows) !Processor row for global row number


   call mpi_comm_rank(mpi_comm_rows,my_prow,mpierr)
   call mpi_comm_size(mpi_comm_rows,np_rows,mpierr)
   call mpi_comm_rank(mpi_comm_cols,my_pcol,mpierr)
   call mpi_comm_size(mpi_comm_cols,np_cols,mpierr)

   ! Matrix is split into tiles; work is done only for tiles on the diagonal or above

   tile_size = nblk*least_common_multiple(np_rows,np_cols) ! minimum global tile size
   tile_size = ((128*max(np_rows,np_cols)-1)/tile_size+1)*tile_size ! make local tiles at least 128 wide

   l_rows_tile = tile_size/np_rows ! local rows of a tile
   l_cols_tile = tile_size/np_cols ! local cols of a tile


   totalblocks = (na-1)/nblk + 1
   max_blocks_row = (totalblocks-1)/np_rows + 1
   max_blocks_col = (totalblocks-1)/np_cols + 1

   max_local_rows = max_blocks_row*nblk
   max_local_cols = max_blocks_col*nblk

   allocate(tmp(MAX(max_local_rows,max_local_cols)))
   allocate(vr(max_local_rows+1))
   allocate(ur(max_local_rows))
   allocate(vc(max_local_cols))
   allocate(uc(max_local_cols))

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#ifdef WITH_OPENMP
   max_threads = omp_get_max_threads()

   allocate(ur_p(max_local_rows,0:max_threads-1))
   allocate(uc_p(max_local_cols,0:max_threads-1))
#endif

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   tmp = 0
   vr = 0
   ur = 0
   vc = 0
   uc = 0

   allocate(vur(max_local_rows,2*max_stored_rows))
   allocate(uvc(max_local_cols,2*max_stored_rows))

   d(:) = 0
   e(:) = 0
   tau(:) = 0

   nstor = 0

   l_rows = local_index(na, my_prow, np_rows, nblk, -1) ! Local rows of a
   l_cols = local_index(na, my_pcol, np_cols, nblk, -1) ! Local cols of a
   if(my_prow==prow(na) .and. my_pcol==pcol(na)) d(na) = a(l_rows,l_cols)

   do istep=na,3,-1

      ! Calculate number of local rows and columns of the still remaining matrix
      ! on the local processor

      l_rows = local_index(istep-1, my_prow, np_rows, nblk, -1)
      l_cols = local_index(istep-1, my_pcol, np_cols, nblk, -1)

      ! Calculate vector for Householder transformation on all procs
      ! owning column istep

      if(my_pcol==pcol(istep)) then

         ! Get vector to be transformed; distribute last element and norm of
         ! remaining elements to all procs in current column

         vr(1:l_rows) = a(1:l_rows,l_cols+1)
         if(nstor>0 .and. l_rows>0) then
            aux(1:2*nstor) = conjg(uvc(l_cols+1,1:2*nstor))
            call ZGEMV('N',l_rows,2*nstor,CONE,vur,ubound(vur,1), &
                       aux,1,CONE,vr,1)
         endif

         if(my_prow==prow(istep-1)) then
            aux1(1) = dot_product(vr(1:l_rows-1),vr(1:l_rows-1))
            aux1(2) = vr(l_rows)
         else
            aux1(1) = dot_product(vr(1:l_rows),vr(1:l_rows))
            aux1(2) = 0.
         endif

         call mpi_allreduce(aux1,aux2,2,MPI_DOUBLE_COMPLEX,MPI_SUM,mpi_comm_rows,mpierr)

         vnorm2 = aux2(1)
         vrl    = aux2(2)

         ! Householder transformation

         call hh_transform_complex(vrl, vnorm2, xf, tau(istep))

         ! Scale vr and store Householder vector for back transformation

         vr(1:l_rows) = vr(1:l_rows) * xf
         if(my_prow==prow(istep-1)) then
            vr(l_rows) = 1.
            e(istep-1) = vrl
         endif
         a(1:l_rows,l_cols+1) = vr(1:l_rows) ! store Householder vector for back transformation

      endif

      ! Broadcast the Householder vector (and tau) along columns

      if(my_pcol==pcol(istep)) vr(l_rows+1) = tau(istep)
      call MPI_Bcast(vr,l_rows+1,MPI_DOUBLE_COMPLEX,pcol(istep),mpi_comm_cols,mpierr)
      tau(istep) =  vr(l_rows+1)

      ! Transpose Householder vector vr -> vc

      call elpa_transpose_vectors  (vr, 2*ubound(vr,1), mpi_comm_rows, &
                                    vc, 2*ubound(vc,1), mpi_comm_cols, &
                                    1, 2*(istep-1), 1, 2*nblk)

      ! Calculate u = (A + VU**T + UV**T)*v

      ! For cache efficiency, we use only the upper half of the matrix tiles for this,
      ! thus the result is partly in uc(:) and partly in ur(:)

      uc(1:l_cols) = 0
      ur(1:l_rows) = 0
      if(l_rows>0 .and. l_cols>0) then
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#ifdef WITH_OPENMP
!$OMP PARALLEL PRIVATE(my_thread,n_threads,n_iter,i,lcs,lce,j,lrs,lre)

         my_thread = omp_get_thread_num()
         n_threads = omp_get_num_threads()

         n_iter = 0

         uc_p(1:l_cols,my_thread) = 0.
         ur_p(1:l_rows,my_thread) = 0.
#endif
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         do i=0,(istep-2)/tile_size
            lcs = i*l_cols_tile+1
            lce = min(l_cols,(i+1)*l_cols_tile)
            if(lce<lcs) cycle
            do j=0,i
               lrs = j*l_rows_tile+1
               lre = min(l_rows,(j+1)*l_rows_tile)
               if(lre<lrs) cycle
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#ifdef WITH_OPENMP
               if(mod(n_iter,n_threads) == my_thread) then
                  call ZGEMV('C',lre-lrs+1,lce-lcs+1,CONE,a(lrs,lcs),lda,vr(lrs),1,CONE,uc_p(lcs,my_thread),1)
                  if(i/=j) call ZGEMV('N',lre-lrs+1,lce-lcs+1,CONE,a(lrs,lcs),lda,vc(lcs),1,CONE,ur_p(lrs,my_thread),1)
               endif
               n_iter = n_iter+1
#else
              call ZGEMV('C',lre-lrs+1,lce-lcs+1,CONE,a(lrs,lcs),lda,vr(lrs),1,CONE,uc(lcs),1)
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               if(i/=j) call ZGEMV('N',lre-lrs+1,lce-lcs+1,CONE,a(lrs,lcs),lda,vc(lcs),1,CONE,ur(lrs),1)
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#endif
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            enddo
         enddo
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#ifdef WITH_OPENMP
!$OMP END PARALLEL

         do i=0,max_threads-1
            uc(1:l_cols) = uc(1:l_cols) + uc_p(1:l_cols,i)
            ur(1:l_rows) = ur(1:l_rows) + ur_p(1:l_rows,i)
         enddo
#endif
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         if(nstor>0) then
            call ZGEMV('C',l_rows,2*nstor,CONE,vur,ubound(vur,1),vr,1,CZERO,aux,1)
            call ZGEMV('N',l_cols,2*nstor,CONE,uvc,ubound