PCUNML2(l)
π PCUNML2 π β the LAPACK routine, explained by your Unix bestie
okay bestie, sit DOWN, because we're talking about PCUNML2, a LAPACK routine (version 1.5, she's vintage, she's iconic, she's from 12 May 1997), and she's about to apply some unitary matrices to your distributed complex matrix and you're gonna THANK her for it. π«Ά
β¨ NAME β¨
PCUNML2 β she overwrites the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with
SIDE = 'L' SIDE = 'R'
TRANS = 'C': Q**H * sub( C ) sub( C ) * Q**H
(and the SIDE = 'L' / SIDE = 'R' / TRANS = 'N' situation is very much part of the whole vibe, we'll get there π
)
πΈ SYNOPSIS πΈ
SUBROUTINE PCUNML2( SIDE, TRANS, M, N, K, A, IA, JA, DESCA, TAU, C, IC,
JC, DESCC, WORK, LWORK, INFO )
CHARACTER SIDE, TRANS
INTEGER IA, IC, INFO, JA, JC, K, LWORK, M, N
INTEGER DESCA( * ), DESCC( * )
COMPLEX A( * ), C( * ), TAU( * ), WORK( * )
π PURPOSE π
PCUNML2 overwrites the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with TRANS = 'C':
Q**H * sub( C )
sub( C ) * Q**H
where Q is a complex unitary distributed matrix defined as the product of K elementary reflectors
Q = H(k)' . . . H(2)' H(1)'
as returned by PCGELQF. Q is of order M if SIDE = 'L' and of order N if SIDE = 'R'. periodt. π
π¦ Notes π¦
Each 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".
okay so here's the whole descriptor situation, no cap:
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 gridAis distributed over. The context itself is global, but the handle (the integer value) may vary.M_A(global) β stored inDESCA( M_ ). The number of rows in the global arrayA.N_A(global) β stored inDESCA( N_ ). The number of columns in the global arrayA.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 arrayAis distributed.CSRC_A(global) β stored inDESCA( CSRC_ ). The process column over which the first column of the arrayAis 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
SIDEβ (global input)CHARACTER.= 'L': applyQorQ**Hfrom the Left;= 'R': applyQorQ**Hfrom the Right.TRANSβ (global input)CHARACTER.= 'N': No transpose, applyQ;= 'C': Conjugate transpose, applyQ**H.Mβ (global input)INTEGER. The number of rows to be operated on i.e the number of rows of the distributed submatrixsub( C ).M >= 0.Nβ (global input)INTEGER. The number of columns to be operated on i.e the number of columns of the distributed submatrixsub( C ).N >= 0.Kβ (global input)INTEGER. The number of elementary reflectors whose product defines the matrixQ. IfSIDE = 'L',M >= K >= 0, ifSIDE = 'R',N >= K >= 0.Aβ (local input)COMPLEXpointer into the local memory to an array of dimension(LLD_A,LOCc(JA+M-1))ifSIDE='L', and(LLD_A,LOCc(JA+N-1))ifSIDE='R', whereLLD_A >= max(1,LOCr(IA+K-1)); On entry, thei-th row must contain the vector which defines the elementary reflectorH(i),IA <= i <= IA+K-1, as returned byPCGELQFin theKrows of its distributed matrix argumentA(IA:IA+K-1,JA:*).
A(IA:IA+K-1,JA:*) is modified by the routine but restored on exit. (she borrows it and gives it BACK, she's polite like that π)
IAβ (global input)INTEGER. The row index in the global arrayAindicating the first row ofsub( A ).JAβ (global input)INTEGER. The column index in the global arrayAindicating the first column ofsub( A ).DESCAβ (global and local input)INTEGERarray of dimensionDLEN_. The array descriptor for the distributed matrixA.TAUβ (local input)COMPLEX, array, dimensionLOCc(IA+K-1). This array contains the scalar factorsTAU(i)of the elementary reflectorsH(i)as returned byPCGELQF.TAUis tied to the distributed matrixA.Cβ (local input/local output)COMPLEXpointer into the local memory to an array of dimension(LLD_C,LOCc(JC+N-1)). On entry, the local pieces of the distributed matrixsub(C). On exit,sub( C )is overwritten byQ*sub( C )orQ'*sub( C )orsub( C )*Q'orsub( C )*Q.ICβ (global input)INTEGER. The row index in the global arrayCindicating the first row ofsub( C ).JCβ (global input)INTEGER. The column index in the global arrayCindicating the first column ofsub( C ).DESCCβ (global and local input)INTEGERarray of dimensionDLEN_. The array descriptor for the distributed matrixC.WORKβ (local workspace/local output)COMPLEXarray, dimension(LWORK). On exit,WORK(1)returns the minimal and optimalLWORK.LWORKβ (local or global input)INTEGER. The dimension of the arrayWORK.LWORKis local input and must be at least:
If SIDE = 'L', LWORK >= MpC0 + MAX( MAX( 1, NqC0 ),
NUMROC( NUMROC( M+IROFFC,MB_A,0,0,NPROW ),MB_A,0,0,LCMP ) );
if SIDE = 'R', LWORK >= NqC0 + MAX( 1, MpC0 );
where
LCMP = LCM / NPROW with LCM = ICLM( NPROW, NPCOL ),
IROFFC = MOD( IC-1, MB_C ), ICOFFC = MOD( JC-1, NB_C ),
ICROW = INDXG2P( IC, MB_C, MYROW, RSRC_C, NPROW ),
ICCOL = INDXG2P( JC, NB_C, MYCOL, CSRC_C, NPCOL ),
MpC0 = NUMROC( M+IROFFC, MB_C, MYROW, ICROW, NPROW ),
NqC0 = NUMROC( N+ICOFFC, NB_C, MYCOL, ICCOL, NPCOL ),
ILCM, INDXG2P and NUMROC are ScaLAPACK tool functions; MYROW, MYCOL, NPROW and NPCOL can be determined by calling the subroutine BLACS_GRIDINFO.
π¨ IfLWORK = -1, thenLWORKis 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 byPXERBLA. (yes,-1is the "just tell me the size bestie" mode π)
INFOβ (local output)INTEGER.= 0: successful exit.< 0: If thei-th argument is an array and thej-entry had an illegal value, thenINFO = -(i*100+j), if thei-th argument is a scalar and had an illegal value, thenINFO = -i.
π Alignment requirements π
The distributed submatrices A(IA:*, JA:*) and C(IC:IC+M-1,JC:JC+N-1) must verify some alignment properties, namely the following expressions should be true:
If SIDE = 'L', ( NB_A.EQ.MB_C .AND. ICOFFA.EQ.IROFFC )
If SIDE = 'R', ( NB_A.EQ.NB_C .AND. ICOFFA.EQ.ICOFFC .AND. IACOL.EQ.ICCOL )
π do NOT skip the alignment check, bestie, she WILL notice and INFO will be negative and you will be sad.
and that's the whole page, mwah π β this was PCUNML2(l) from LAPACK version 1.5, dated 12 May 1997. vintage queen behavior, honestly. go forth and multiply your unitary matrices responsibly. 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