PDLAQGE(l)

πŸ“¦ version 1.5, in ubuntu jammy

πŸ’– NAME πŸ’–

PDLAQGE β€” bestie, this routine equilibrates a general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) using the row and scaling factors chilling in the vectors R and C. That's it, that's the whole job. πŸ’…


🌸 SYNOPSIS 🌸

SUBROUTINE PDLAQGE( M, N, A, IA, JA, DESCA, R, C, ROWCND, COLCND, AMAX,
                    EQUED )

    CHARACTER       EQUED

    INTEGER         IA, JA, M, N

    DOUBLE          PRECISION AMAX, COLCND, ROWCND

    INTEGER         DESCA( * )

    DOUBLE          PRECISION A( * ), C( * ), R( * )

πŸŽ€ PURPOSE πŸŽ€

okay so. PDLAQGE equilibrates a general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) using the row and scaling factors in the vectors R and C. literally its whole personality. πŸ¦‹

✨ Notes ✨

so every global data object has a lil description vector attached to it, right? That vector is the one holding all the info you need to map an object element to its corresponding process and memory location. iykyk. 🧚

Let A be a generic term for any 2D block cyclicly distributed array. Such a global array has an associated description vector DESCA. In the following comments, the character _ should be read as "of the global array". (yes bestie, read it like a little suffix, it's giving very much "A's descriptor".) πŸ’œ

  • DTYPE_A (global) β€” stored in DESCA( DTYPE_ ): the descriptor type. In this case, DTYPE_A = 1.
  • CTXT_A (global) β€” stored in DESCA( CTXT_ ): the BLACS context handle, indicating the BLACS process grid A is distributed over. The context itself is global, but the handle (the integer value) may vary. πŸ™ˆ
  • M_A (global) β€” stored in DESCA( M_ ): the number of rows in the global array A.
  • N_A (global) β€” stored in DESCA( N_ ): the number of columns in the global array A.
  • MB_A (global) β€” stored in DESCA( MB_ ): the blocking factor used to distribute the rows of the array.
  • NB_A (global) β€” stored in DESCA( NB_ ): the blocking factor used to distribute the columns of the array.
  • RSRC_A (global) β€” stored in DESCA( RSRC_ ): the process row over which the first row of the array A is distributed.
  • CSRC_A (global) β€” stored in DESCA( CSRC_ ): the process column over which the first column of the array A is distributed.
  • LLD_A (local) β€” stored in DESCA( LLD_ ): the leading dimension of the local array. LLD_A >= MAX(1,LOCr(M_A)).

Let K be the number of rows or columns of a distributed matrix, and assume that its process grid has dimension p x q. LOCr( K ) denotes the number of elements of K that a process would receive if K were distributed over the p processes of its process column. Similarly, LOCc( K ) denotes the number of elements of K that a process would receive if K were distributed over the q processes of its process row.

The values of LOCr() and LOCc() may be determined via a call to the ScaLAPACK tool function, NUMROC:

LOCr( M ) = NUMROC( M, MB_A, MYROW, RSRC_A, NPROW ),
LOCc( N ) = NUMROC( N, NB_A, MYCOL, CSRC_A, NPCOL ).

An upper bound for these quantities may be computed by:

LOCr( M ) <= ceil( ceil(M/MB_A)/NPROW )*MB_A
LOCc( N ) <= ceil( ceil(N/NB_A)/NPCOL )*NB_A

πŸ‘‘ ARGUMENTS πŸ‘‘

  • M (global input) INTEGER β€” the number of rows to be operated on, i.e. the number of rows of the distributed submatrix sub( A ). M >= 0. no negativity allowed babe. 🚫
  • N (global input) INTEGER β€” the number of columns to be operated on, i.e. the number of columns of the distributed submatrix sub( A ). N >= 0.
  • A (local input/local output) DOUBLE PRECISION β€” pointer into the local memory to an array of dimension (LLD_A,LOCc(JA+N-1)) containing on entry the M-by-N matrix sub( A ). On exit, the equilibrated distributed matrix. See EQUED for the form of the equilibrated distributed submatrix. she comes in one way and leaves iconic. ✨
  • IA (global input) INTEGER β€” the row index in the global array A indicating the first row of sub( A ).
  • JA (global input) INTEGER β€” the column index in the global array A indicating the first column of sub( A ).
  • DESCA (global and local input) INTEGER array of dimension DLEN_. The array descriptor for the distributed matrix A.
  • R (local input) DOUBLE PRECISION array, dimension LOCr(M_A) β€” the row scale factors for sub( A ). R is aligned with the distributed matrix A, and replicated across every process column. R is tied to the distributed matrix A. they're bonded fr fr. 🫢
  • C (local input) DOUBLE PRECISION array, dimension LOCc(N_A) β€” the column scale factors of sub( A ). C is aligned with the distributed matrix A, and replicated down every process row. C is tied to the distributed matrix A. also bonded. 🫢
  • ROWCND (global input) DOUBLE PRECISION β€” the global ratio of the smallest R(i) to the largest R(i), IA <= i <= IA+M-1.
  • COLCND (global input) DOUBLE PRECISION β€” the global ratio of the smallest C(i) to the largest C(i), JA <= j <= JA+N-1.
  • AMAX (global input) DOUBLE PRECISION β€” absolute value of largest distributed submatrix entry.
  • EQUED (global output) CHARACTER β€” specifies the form of equilibration that was done. the vibe report, basically:
    • = 'N': No equilibration
    • = 'R': Row equilibration, i.e., sub( A ) has been pre-multiplied by diag(R(IA:IA+M-1)),
    • = 'C': Column equilibration, i.e., sub( A ) has been post-multiplied by diag(C(JA:JA+N-1)),
    • = 'B': Both row and column equilibration, i.e., sub( A ) has been replaced by diag(R(IA:IA+M-1)) * sub( A ) * diag(C(JA:JA+N-1)). both?? she did BOTH. main character energy, periodt. πŸ’…πŸ”₯

πŸ“ PARAMETERS πŸ“

  • THRESH is a threshold value used to decide if row or column scaling should be done based on the ratio of the row or column scaling factors. If ROWCND < THRESH, row scaling is done, and if COLCND < THRESH, column scaling is done. the cutoff queen. πŸ‘‘
  • LARGE and SMALL are threshold values used to decide if row scaling should be done based on the absolute size of the largest matrix element. If AMAX > LARGE or AMAX < SMALL, row scaling is done. big or tiny, either way she's scaling. 😭
ngl bestie, these threshold cutoffs are the whole decision tree β€” everything else is just vibes and block cyclic distribution. no cap. πŸ’€

and that's the tea πŸŽ€ this page is from LAPACK version 1.5, dated 12 May 1997 β€” like, an actual vintage. it ate and left no crumbs. πŸ’–βœ¨πŸŒˆ

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