PDLAQGE(l)
π NAME π
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. π
πΈ SYNOPSIS πΈ
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( * )
π PURPOSE π
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. π¦
β¨ Notes β¨
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. π§
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".) π
DTYPE_A(global) β stored inDESCA( DTYPE_ ): the descriptor type. In this case,DTYPE_A = 1.CTXT_A(global) β stored inDESCA( 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. πM_A(global) β stored inDESCA( M_ ): the number of rows in the global array A.N_A(global) β stored inDESCA( N_ ): the number of columns in the global array A.MB_A(global) β stored inDESCA( MB_ ): the blocking factor used to distribute the rows of the array.NB_A(global) β stored inDESCA( NB_ ): the blocking factor used to distribute the columns of the array.RSRC_A(global) β stored inDESCA( RSRC_ ): the process row over which the first row of the array A is distributed.CSRC_A(global) β stored inDESCA( CSRC_ ): the process column over which the first column of the array A is distributed.LLD_A(local) β stored inDESCA( 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
π ARGUMENTS π
M(global input)INTEGERβ the number of rows to be operated on, i.e. the number of rows of the distributed submatrixsub( A ).M >= 0. no negativity allowed babe. π«N(global input)INTEGERβ the number of columns to be operated on, i.e. the number of columns of the distributed submatrixsub( A ).N >= 0.A(local input/local output)DOUBLE PRECISIONβ pointer into the local memory to an array of dimension(LLD_A,LOCc(JA+N-1))containing on entry the M-by-N matrixsub( A ). On exit, the equilibrated distributed matrix. SeeEQUEDfor the form of the equilibrated distributed submatrix. she comes in one way and leaves iconic. β¨IA(global input)INTEGERβ the row index in the global arrayAindicating the first row ofsub( A ).JA(global input)INTEGERβ the column index in the global arrayAindicating the first column ofsub( A ).DESCA(global and local input)INTEGERarray of dimensionDLEN_. The array descriptor for the distributed matrixA.R(local input)DOUBLE PRECISIONarray, dimensionLOCr(M_A)β the row scale factors forsub( A ).Ris aligned with the distributed matrixA, and replicated across every process column.Ris tied to the distributed matrixA. they're bonded fr fr. π«ΆC(local input)DOUBLE PRECISIONarray, dimensionLOCc(N_A)β the column scale factors ofsub( A ).Cis aligned with the distributed matrixA, and replicated down every process row.Cis tied to the distributed matrixA. also bonded. π«ΆROWCND(global input)DOUBLE PRECISIONβ the global ratio of the smallestR(i)to the largestR(i),IA <= i <= IA+M-1.COLCND(global input)DOUBLE PRECISIONβ the global ratio of the smallestC(i)to the largestC(i),JA <= j <= JA+N-1.AMAX(global input)DOUBLE PRECISIONβ absolute value of largest distributed submatrix entry.EQUED(global output)CHARACTERβ 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 bydiag(R(IA:IA+M-1)),= 'C': Column equilibration, i.e.,sub( A )has been post-multiplied bydiag(C(JA:JA+N-1)),= 'B': Both row and column equilibration, i.e.,sub( A )has been replaced bydiag(R(IA:IA+M-1)) * sub( A ) * diag(C(JA:JA+N-1)). both?? she did BOTH. main character energy, periodt. π π₯
π PARAMETERS π
THRESHis 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. IfROWCND < THRESH, row scaling is done, and ifCOLCND < THRESH, column scaling is done. the cutoff queen. πLARGEandSMALLare threshold values used to decide if row scaling should be done based on the absolute size of the largest matrix element. IfAMAX > LARGEorAMAX < SMALL, row scaling is done. big or tiny, either way she's scaling. π
ngl bestie, these threshold cutoffs are the whole decision tree β everything else is just vibes and block cyclic distribution. no cap. π
and that's the tea π this page is from LAPACK version 1.5, dated 12 May 1997 β like, an actual vintage. it ate and left no crumbs. πβ¨π
π¦ where this page comes from
- ubuntu jammy: scalapack-doc 1.5-11
- π girlified by deepseek-ai/DeepSeek-V4.1-Flash on : a language model's rewrite of the original, under its terms