PDGERFS(l)
π PDGERFS π
okay bestie, sit down, hydrate, because this one is a baddie. This is PDGERFS from LAPACK, version 1.5, and it's the glow-up routine for your linear systems. You already solved something? Cute. Now let it improve that computed solution and hand you error bounds and backward error estimates on the way out. The way it does all that and still returns on time? Obsessed. β¨
πΈ NAME πΈ
PDGERFS β improves the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solutions. no cap, that's literally the whole job description.
π SYNOPSIS π
SUBROUTINE PDGERFS( TRANS, N, NRHS, A, IA, JA, DESCA, AF, IAF, JAF,
DESCAF, IPIV, B, IB, JB, DESCB, X, IX, JX, DESCX,
FERR, BERR, WORK, LWORK, IWORK, LIWORK, INFO )
CHARACTER TRANS
INTEGER IA, IAF, IB, IX, INFO, JA, JAF, JB, JX, LIWORK,
LWORK, N, NRHS
INTEGER DESCA( * ), DESCAF( * ), DESCB( * ), DESCX( *
),IPIV( * ), IWORK( * )
DOUBLE PRECISION A( * ), AF( * ), B( * ), BERR( * ), FERR(
* ), WORK( * ), X( * )
β¨ PURPOSE β¨
PDGERFS improves the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solutions. that's it, that's the mission statement, and she executes π
π Notes π
Every global data object is described by an associated description vector. This vector stores the information required to establish the mapping between an object element and its corresponding process and memory location.
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". π¦
Here's the whole notation breakdown, and bestie I am not skipping a single line of it:
DTYPE_A(global) β stored inDESCA( DTYPE_ )β the descriptor type. In this case,DTYPE_A = 1.CTXT_A(global) β stored inDESCA( CTXT_ )β the BLACS context handle, indicating the BLACS process gridAis distributed over. The context itself is global, but the handle (the integer value) may vary.M_A(global) β stored inDESCA( M_ )β the number of rows in the global arrayA.N_A(global) β stored inDESCA( N_ )β the number of columns in the global arrayA.MB_A(global) β stored inDESCA( MB_ )β the blocking factor used to distribute the rows of the array.NB_A(global) β stored inDESCA( NB_ )β the blocking factor used to distribute the columns of the array.RSRC_A(global) β stored inDESCA( RSRC_ )β the process row over which the first row of the arrayAis distributed.CSRC_A(global) β stored inDESCA( CSRC_ )β the process column over which the first column of the arrayAis distributed.LLD_A(local) β stored inDESCA( LLD_ )β 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
In the following comments, sub( A ), sub( X ) and sub( B ) denote respectively A(IA:IA+N-1,JA:JA+N-1), X(IX:IX+N-1,JX:JX+NRHS-1) and B(IB:IB+N-1,JB:JB+NRHS-1). slay, notation handled. π
π ARGUMENTS π
TRANS(global input)CHARACTER*1β specifies the form of the system of equations:
= 'N': sub( A ) * sub( X ) = sub( B ) (No transpose)
= 'T': sub( A )**T * sub( X ) = sub( B ) (Transpose)
= 'C': sub( A )**T * sub( X ) = sub( B ) (Conjugate transpose = Transpose)
N(global input)INTEGERβ the order of the matrixsub( A ).N >= 0.NRHS(global input)INTEGERβ the number of right hand sides, i.e., the number of columns of the matricessub( B )andsub( X ).NRHS >= 0.A(local input)DOUBLE PRECISIONβ pointer into the local memory to an array of local dimension(LLD_A,LOCc(JA+N-1)). This array contains the local pieces of the distributed matrixsub( A ).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)INTEGER array of dimension DLEN_β the array descriptor for the distributed matrixA.AF(local input)DOUBLE PRECISIONβ pointer into the local memory to an array of local dimension(LLD_AF,LOCc(JA+N-1)). This array contains the local pieces of the distributed factors of the matrixsub( A ) = P * L * Uas computed byPDGETRF.IAF(global input)INTEGERβ the row index in the global arrayAFindicating the first row ofsub( AF ).JAF(global input)INTEGERβ the column index in the global arrayAFindicating the first column ofsub( AF ).DESCAF(global and local input)INTEGER array of dimension DLEN_β the array descriptor for the distributed matrixAF.IPIV(local input)INTEGER array of dimension LOCr(M_AF)+MB_AFβ this array contains the pivoting information as computed byPDGETRF.IPIV(i)-> the global row local rowiwas swapped with. This array is tied to the distributed matrixA.B(local input)DOUBLE PRECISIONβ pointer into the local memory to an array of local dimension(LLD_B,LOCc(JB+NRHS-1)). This array contains the local pieces of the distributed matrix of right hand sidessub( B ).IB(global input)INTEGERβ the row index in the global arrayBindicating the first row ofsub( B ).JB(global input)INTEGERβ the column index in the global arrayBindicating the first column ofsub( B ).DESCB(global and local input)INTEGER array of dimension DLEN_β the array descriptor for the distributed matrixB.X(local input and output)DOUBLE PRECISIONβ pointer into the local memory to an array of local dimension(LLD_X,LOCc(JX+NRHS-1)). On entry, this array contains the local pieces of the distributed matrix solutionsub( X ). On exit, the improved solution vectors. glow-up complete πIX(global input)INTEGERβ the row index in the global arrayXindicating the first row ofsub( X ).JX(global input)INTEGERβ the column index in the global arrayXindicating the first column ofsub( X ).DESCX(global and local input)INTEGER array of dimension DLEN_β the array descriptor for the distributed matrixX.FERR(local output)DOUBLE PRECISION array of local dimension LOCc(JB+NRHS-1)β the estimated forward error bound for each solution vector ofsub( X ). IfXTRUEis the true solution corresponding tosub( X ),FERRis an estimated upper bound for the magnitude of the largest element in(sub( X ) - XTRUE)divided by the magnitude of the largest element insub( X ). The estimate is as reliable as the estimate forRCOND, and is almost always a slight overestimate of the true error. This array is tied to the distributed matrixX.BERR(local output)DOUBLE PRECISION array of local dimension LOCc(JB+NRHS-1)β the componentwise relative backward error of each solution vector (i.e., the smallest relative change in any entry ofsub( A )orsub( B )that makessub( X )an exact solution). This array is tied to the distributed matrixX.WORK(local workspace/local output)DOUBLE PRECISION array, dimension (LWORK)β on exit,WORK(1)returns the minimal and optimalLWORK.LWORK(local or global input)INTEGERβ the dimension of the arrayWORK.LWORKis local input and must be at least
LWORK >= 3*LOCr( N + MOD(IA-1,MB_A) )
If LWORK = -1, then LWORK is global input and a workspace query is assumed; the routine only calculates the minimum and optimal size for all work arrays. Each of these values is returned in the first entry of the corresponding work array, and no error message is issued by PXERBLA.
IWORK(local workspace/local output)INTEGER array, dimension (LIWORK)β on exit,IWORK(1)returns the minimal and optimalLIWORK.LIWORK(local or global input)INTEGERβ the dimension of the arrayIWORK.LIWORKis local input and must be at least
LIWORK >= LOCr( N + MOD(IB-1,MB_B) ).
If LIWORK = -1, then LIWORK is global input and a workspace query is assumed; the routine only calculates the minimum and optimal size for all work arrays. Each of these values is returned in the first entry of the corresponding work array, and no error message is issued by PXERBLA.
INFO(global output)INTEGERβ
= 0: successful exit
< 0: If the i-th argument is an array and the j-entry had an
illegal value, then INFO = -(i*100+j), if the i-th argument is a
scalar and had an illegal value, then INFO = -i.
π«Ά PARAMETERS π«Ά
ITMAX is the maximum number of steps of iterative refinement. queen of persistence, honestly π₯
π Notes π
This routine temporarily returns when N <= 1. (tiny problem? tiny nap. π΄)
The distributed submatrices op( A ) and op( AF ) (respectively sub( X ) and sub( B )) should be distributed the same way on the same processes. These conditions ensure that sub( A ) and sub( AF ) (resp. sub( X ) and sub( B )) are "perfectly" aligned. matching outfits, matching grids π¦
Moreover, this routine requires the distributed submatricessub( A ),sub( AF ),sub( X ), andsub( B )to be aligned on a block boundary, i.e., iff(x,y) = MOD( x-1, y ):
f( IA, DESCA( MB_ ) ) = f( JA, DESCA( NB_ ) ) = 0,
f( IAF, DESCAF( MB_ ) ) = f( JAF, DESCAF( NB_ ) ) = 0,
f( IB, DESCB( MB_ ) ) = f( JB, DESCB( NB_ ) ) = 0, and
f( IX, DESCX( MB_ ) ) = f( JX, DESCX( NB_ ) ) = 0.
and that's the whole page bestie, you ate it π β¨
this was PDGERFS from LAPACK version 1.5, dated 12 May 1997 β an absolute icon of the PDGERFS(l) manual-page era, and yes, the vintage is part of the charm ππ¦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