PCHEEVX(l)
π PCHEEVX(l) π
okay so this is PCHEEVX from LAPACK routine (version 1.5), bestie, and it's a whole mood. It's a ScaLAPACK routine and it's lowkey iconic. Buckle up, there's a lot. π
β¨ NAME β¨
PCHEEVX β compute selected eigenvalues and, optionally, eigenvectors of a complex hermitian matrix A by calling the recommended sequence of ScaLAPACK routines.
π SYNOPSIS π
SUBROUTINE PCHEEVX( JOBZ, RANGE, UPLO, N, A, IA, JA, DESCA, VL, VU, IL,
IU, ABSTOL, M, NZ, W, ORFAC, Z, IZ, JZ, DESCZ, WORK,
LWORK, RWORK, LRWORK, IWORK, LIWORK, IFAIL, ICLUSTR,
GAP, INFO )
CHARACTER JOBZ, RANGE, UPLO
INTEGER IA, IL, INFO, IU, IZ, JA, JZ, LIWORK, LRWORK, LWORK,
M, N, NZ
REAL ABSTOL, ORFAC, VL, VU
INTEGER DESCA( * ), DESCZ( * ), ICLUSTR( * ), IFAIL( * ),
IWORK( * )
REAL GAP( * ), RWORK( * ), W( * )
COMPLEX A( * ), WORK( * ), Z( * )
INTEGER BLOCK_CYCLIC_2D, DLEN_, DTYPE_, CTXT_, M_, N_, MB_,
NB_, RSRC_, CSRC_, LLD_
PARAMETER ( BLOCK_CYCLIC_2D = 1, DLEN_ = 9, DTYPE_ = 1, CTXT_
= 2, M_ = 3, N_ = 4, MB_ = 5, NB_ = 6, RSRC_ = 7,
CSRC_ = 8, LLD_ = 9 )
REAL ZERO, ONE, TEN, FIVE
PARAMETER ( ZERO = 0.0E+0, ONE = 1.0E+0, TEN = 10.0E+0, FIVE =
5.0E+0 )
INTEGER IERREIN, IERRCLS, IERRSPC, IERREBZ
PARAMETER ( IERREIN = 1, IERRCLS = 2, IERRSPC = 4, IERREBZ = 8
)
LOGICAL ALLEIG, INDEIG, LOWER, LQUERY, QUICKRETURN, VALEIG,
WANTZ
CHARACTER ORDER
INTEGER CSRC_A, I, IACOL, IAROW, ICOFFA, IINFO, INDD, INDD2,
INDE, INDE2, INDIBL, INDISP, INDRWORK, INDTAU,
INDWORK, IROFFA, IROFFZ, ISCALE, ISIZESTEBZ,
ISIZESTEIN, IZROW, LALLWORK, LIWMIN, LLRWORK,
LLWORK, LRWMIN, LWMIN, MAXEIGS, MB_A, MB_Z, MQ0,
MYCOL, MYROW, NB, NB_A, NB_Z, NEIG, NN, NNP, NP0,
NPCOL, NPROCS, NPROW, NQ0, NSPLIT, NZZ, OFFSET,
RSRC_A, RSRC_Z, SIZEHEEVX, SIZEORMTR, SIZESTEIN
REAL ABSTLL, ANRM, BIGNUM, EPS, RMAX, RMIN, SAFMIN,
SIGMA, SMLNUM, VLL, VUU
INTEGER IDUM1( 4 ), IDUM2( 4 )
LOGICAL LSAME
INTEGER ICEIL, INDXG2P, NUMROC
REAL PCLANHE, PSLAMCH
EXTERNAL LSAME, ICEIL, INDXG2P, NUMROC, PCLANHE, PSLAMCH
EXTERNAL BLACS_GRIDINFO, CHK1MAT, IGAMN2D, PCELGET, PCHETRD,
PCHK2MAT, PCLASCL, PCSTEIN, PCUNMTR, PSLARED1D,
PSSTEBZ, PXERBLA, SGEBR2D, SGEBS2D, SLASRT, SSCAL
INTRINSIC ABS, CMPLX, ICHAR, MAX, MIN, MOD, REAL, SQRT
π PURPOSE π
PCHEEVX computes selected eigenvalues and, optionally, eigenvectors of a complex hermitian matrix A by calling the recommended sequence of ScaLAPACK routines. β¨
And the way it selects? You can pick eigenvalues/vectors either by specifying a range of values or a range of indices for the eigenvalues you want. Your choice, bestie, main character energy fr fr. π
π¦ NOTES π¦
okay so every global data object has a whole description vector situation going on. This vector stores all the info you need to establish the mapping between an object element and its corresponding process and memory location. π«Ά
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".
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.S πΈ
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
PCHEEVX assumes IEEE 754 standard compliant arithmetic. π To port to a system which does not have IEEE 754 arithmetic, modify the appropriate SLmake.inc file to include the compiler switch -DNO_IEEE. This switch only affects the compilation of pslaiect.c.
π₯ ARGUMENTS π₯
Heads up:NP= the number of rows local to a given process.NQ= the number of columns local to a given process.
JOBZ β (global input) CHARACTER*1
Specifies whether or not to compute the eigenvectors:
= 'N': Compute eigenvalues only.= 'V': Compute eigenvalues and eigenvectors.
RANGE β (global input) CHARACTER*1
= 'A': all eigenvalues will be found.= 'V': all eigenvalues in the interval[VL,VU]will be found.= 'I': theIL-th throughIU-th eigenvalues will be found.
UPLO β (global input) CHARACTER*1
Specifies whether the upper or lower triangular part of the Hermitian matrix A is stored:
= 'U': Upper triangular= 'L': Lower triangular
N β (global input) INTEGER
The number of rows and columns of the matrix A. N >= 0. π
A β (local input/workspace) block cyclic COMPLEX array
global dimension (N, N), local dimension ( LLD_A, LOCc(JA+N-1) )
On entry, the Hermitian matrix A. If UPLO = 'U', only the upper triangular part of A is used to define the elements of the Hermitian matrix. If UPLO = 'L', only the lower triangular part of A is used to define the elements of the Hermitian matrix.
π On exit, the lower triangle (ifUPLO='L') or the upper triangle (ifUPLO='U') ofA, including the diagonal, is destroyed. Not a drill.
IA β (global input) INTEGER
A's global row index, which points to the beginning of the submatrix which is to be operated on.
JA β (global input) INTEGER
A's global column index, which points to the beginning of the submatrix which is to be operated on.
DESCA β (global and local input) INTEGER array of dimension DLEN_
The array descriptor for the distributed matrix A.
Careful bestie: ifDESCA( CTXT_ )is incorrect,PCHEEVXcannot guarantee correct error reporting. π
VL β (global input) REAL
If RANGE='V', the lower bound of the interval to be searched for eigenvalues. Not referenced if RANGE = 'A' or 'I'.
VU β (global input) REAL
If RANGE='V', the upper bound of the interval to be searched for eigenvalues. Not referenced if RANGE = 'A' or 'I'.
IL β (global input) INTEGER
If RANGE='I', the index (from smallest to largest) of the smallest eigenvalue to be returned. IL >= 1. Not referenced if RANGE = 'A' or 'V'.
IU β (global input) INTEGER
If RANGE='I', the index (from smallest to largest) of the largest eigenvalue to be returned. min(IL,N) <= IU <= N. Not referenced if RANGE = 'A' or 'V'.
ABSTOL β (global input) REAL
this one's a whole paragraph, stay with me π
If JOBZ='V', setting ABSTOL to PSLAMCH( CONTEXT, 'U') yields the most orthogonal eigenvectors.
The absolute error tolerance for the eigenvalues. An approximate eigenvalue is accepted as converged when it is determined to lie in an interval [a,b] of width less than or equal to
ABSTOL + EPS * max( |a|,|b| ) ,
where EPS is the machine precision. If ABSTOL is less than or equal to zero, then EPS*norm(T) will be used in its place, where norm(T) is the 1-norm of the tridiagonal matrix obtained by reducing A to tridiagonal form.
Eigenvalues will be computed most accurately when ABSTOL is set to twice the underflow threshold 2*PSLAMCH('S'), not zero. If this routine returns with ((MOD(INFO,2).NE.0) .OR. (MOD(INFO/8,2).NE.0)), indicating that some eigenvalues or eigenvectors did not converge, try setting ABSTOL to 2*PSLAMCH('S'). π
See "Computing Small Singular Values of Bidiagonal Matrices with Guaranteed High Relative Accuracy," by Demmel and Kahan, LAPACK Working Note #3.
See "On the correctness of Parallel Bisection in Floating Point" by Demmel, Dhillon and Ren, LAPACK Working Note #70.
M β (global output) INTEGER
Total number of eigenvalues found. 0 <= M <= N. β¨
NZ(global output)INTEGERβ okay bestie, this is the total number of eigenvectors computed.0 <= NZ <= M. Itβs literally the number of columns ofZthat are filled. IfJOBZ .NE. 'V',NZis not referenced. IfJOBZ .EQ. 'V', thenNZ = Munless the user supplies insufficient space andPCHEEVXis not able to detect this before beginning computation. To get all the eigenvectors requested, the user must supply both sufficient space to hold the eigenvectors inZ(M .LE. DESCZ(N_)) and sufficient workspace to compute them. (SeeLWORKbelow.)PCHEEVXis always able to detect insufficient space without computation unlessRANGE .EQ. 'V'. πW(global output)REAL array, dimension (N)β on normal exit, the firstMentries contain the selected eigenvalues in ascending order. Periodt. β¨ORFAC(global input)REALβ specifies which eigenvectors should be reorthogonalized. Eigenvectors that correspond to eigenvalues which are withintol=ORFAC*norm(A)of each other are to be reorthogonalized. However, if the workspace is insufficient (seeLWORK),tolmay be decreased until all eigenvectors to be reorthogonalized can be stored in one process. No reorthogonalization will be done ifORFACequals zero. A default value of10^-3is used ifORFACis negative.ORFACshould be identical on all processes. π«ΆZ(local output)COMPLEX array, global dimension (N, N), local dimension ( LLD_Z, LOCc(JZ+N-1) )β ifJOBZ = 'V', then on normal exit the firstMcolumns ofZcontain the orthonormal eigenvectors of the matrix corresponding to the selected eigenvalues. If an eigenvector fails to converge, then that column ofZcontains the latest approximation to the eigenvector, and the index of the eigenvector is returned inIFAIL. IfJOBZ = 'N', thenZis not referenced. πIZ(global input)INTEGERβZβs global row index, which points to the beginning of the submatrix which is to be operated on. β¨JZ(global input)INTEGERβZβs global column index, which points to the beginning of the submatrix which is to be operated on. πΈDESCZ(global and local input)INTEGER array of dimension DLEN_β the array descriptor for the distributed matrixZ.DESCZ( CTXT_ )must equalDESCA( CTXT_ ). No cap. πWORK(local workspace/output)COMPLEX array, dimension (LWORK)βWORK(1)returns workspace adequate workspace to allow optimal performance. (Yes, it says βadequate workspaceβ, weβre keeping it exactly.) πLWORK(local input)INTEGERβ size ofWORKarray. If only eigenvalues are requested:
LWORK >= N + MAX( NB * ( NP0 + 1 ), 3 )
If eigenvectors are requested:
LWORK >= N + ( NP0 + MQ0 + NB ) * NB
with NQ0 = NUMROC( NN, NB, 0, 0, NPCOL ).
For optimal performance, greater workspace is needed, i.e.
LWORK >= MAX( LWORK, NHETRD_LWORK )
Where LWORK is as defined above, and
NHETRD_LWORK = N + 2*( ANB+1 )*( 4*NPS+2 ) +
( NPS + 1 ) * NPS
ICTXT = DESCA( CTXT_ )
ANB = PJLAENV( ICTXT, 3, 'PCHETTRD', 'L', 0, 0, 0, 0 )
SQNPC = SQRT( DBLE( NPROW * NPCOL ) )
NPS = MAX( NUMROC( N, 1, 0, 0, SQNPC ), 2*ANB )
NUMROC is a ScaLAPACK tool functions; PJLAENV is a ScaLAPACK envionmental inquiry function. MYROW, MYCOL, NPROW and NPCOL can be determined by calling the subroutine BLACS_GRIDINFO.
If LWORK = -1, then LWORK is global input and a workspace query is assumed; the routine only calculates the 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. π₯
RWORK(local workspace/output)REAL array, dimension (LRWORK)β on return,WROK(1)contains the optimal amount of workspace required for efficient execution. (Not a typo of mine, bestie β itβsWROK(1)in the original.) IfJOBZ='N',RWORK(1)= optimal amount of workspace required to compute eigenvalues efficiently. IfJOBZ='V',RWORK(1)= optimal amount of workspace required to compute eigenvalues and eigenvectors efficiently with no guarantee on orthogonality. IfRANGE='V', it is assumed that all eigenvectors may be required. π¦LRWORK(local input)INTEGERβ size ofRWORK. See below for definitions of variables used to defineLRWORK.
If no eigenvectors are requested (JOBZ = 'N') then:
LRWORK >= 5 * NN + 4 * N
If eigenvectors are requested (JOBZ = 'V' ) then the amount of workspace required to guarantee that all eigenvectors are computed is:
LRWORK >= 4*N + MAX( 5*NN, NP0 * MQ0 ) +
ICEIL( NEIG, NPROW*NPCOL)*NN
The computed eigenvectors may not be orthogonal if the minimal workspace is supplied and ORFAC is too small. If you want to guarantee orthogonality (at the cost of potentially poor performance) you should add the following to LRWORK:
(CLUSTERSIZE-1)*N
where CLUSTERSIZE is the number of eigenvalues in the largest cluster, where a cluster is defined as a set of close eigenvalues: { W(K),...,W(K+CLUSTERSIZE-1) | W(J+1) <= W(J) + ORFAC*2*norm(A) }
Variable definitions:
NEIG = number of eigenvectors requested
NB = DESCA( MB_ ) = DESCA( NB_ ) =
DESCZ( MB_ ) = DESCZ( NB_ )
NN = MAX( N, NB, 2 )
DESCA( RSRC_ ) = DESCA( NB_ ) = DESCZ( RSRC_ ) =
DESCZ( CSRC_ ) = 0
NP0 = NUMROC( NN, NB, 0, 0, NPROW )
MQ0 = NUMROC( MAX( NEIG, NB, 2 ), NB, 0, 0, NPCOL )
ICEIL( X, Y ) is a ScaLAPACK function returning
ceiling(X/Y)
When LRWORK is too small:
- If
LRWORKis too small to guarantee orthogonality,PCHEEVXattempts to maintain orthogonality in the clusters with the smallest spacing between the eigenvalues. - If
LRWORKis too small to compute all the eigenvectors requested, no computation is performed andINFO=-25is returned. Note that whenRANGE='V',PCHEEVXdoes not know how many eigenvectors are requested until the eigenvalues are computed. Therefore, whenRANGE='V'and as long asLRWORKis large enough to allowPCHEEVXto compute the eigenvalues,PCHEEVXwill compute the eigenvalues and as many eigenvectors as it can.
Relationship between workspace, orthogonality & performance:
- If
CLUSTERSIZE >= N/SQRT(NPROW*NPCOL), then providing enough space to compute all the eigenvectors orthogonally will cause serious degradation in performance. In the limit (i.e.CLUSTERSIZE = N-1)PCSTEINwill perform no better thanCSTEINon 1 processor. - For
CLUSTERSIZE = N/SQRT(NPROW*NPCOL)reorthogonalizing all eigenvectors will increase the total execution time by a factor of 2 or more. - For
CLUSTERSIZE > N/SQRT(NPROW*NPCOL)execution time will grow as the square of the cluster size, all other factors remaining equal and assuming enough workspace. Less workspace means less reorthogonalization but faster execution.
If LRWORK = -1, then LRWORK is global input and a workspace query is assumed; the routine only calculates the size required for optimal performance for all work arrays. Each of these values is returned in the first entry of the corresponding work arrays, and no error message is issued by PXERBLA. π
IWORK(local workspace)INTEGER arrayβ on return,IWORK(1)contains the amount of integer workspace required. πLIWORK(local input)INTEGERβ size ofIWORK.LIWORK >= 6 * NNP. Where:
NNP = MAX( N, NPROW*NPCOL + 1, 4 )
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. β¨
IFAIL(global output)INTEGER array, dimension (N)β ifJOBZ = 'V', then on normal exit, the firstMelements ofIFAILare zero. If(MOD(INFO,2).NE.0)on exit, thenIFAILcontains the indices of the eigenvectors that failed to converge. IfJOBZ = 'N', thenIFAILis not referenced. πICLUSTR(global output)integer array, dimension (2*NPROW*NPCOL)β this array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not be reorthogonalized due to insufficient workspace (seeLWORK,ORFACandINFO). Eigenvectors corresponding to clusters of eigenvalues indexedICLUSTR(2*I-1)toICLUSTR(2*I), could not be reorthogonalized due to lack of workspace. Hence the eigenvectors corresponding to these clusters may not be orthogonal.ICLUSTR()is a zero terminated array.(ICLUSTR(2*K).NE.0 .AND. ICLUSTR(2*K+1).EQ.0)if and only ifKis the number of clusters.ICLUSTRis not referenced ifJOBZ = 'N'. πGAP(global output)REAL array, dimension (NPROW*NPCOL)β this array contains the gap between eigenvalues whose eigenvectors could not be reorthogonalized. The output values in this array correspond to the clusters indicated by the arrayICLUSTR. As a result, the dot product between eigenvectors correspoding to theI^thcluster may be as high as( C * n ) / GAP(I)whereCis a small constant. πINFO(global output)INTEGERβ the receipts bestie, this is where all the tea gets spilled about how the run went π= 0β we ate. Clean exit, everything's fine, no notes. β¨< 0β uh oh, something got passed in wrong π. If the i-th argument is an array and the j-entry had an illegal value, thenINFO = -(i*100+j); if the i-th argument is a scalar and had an illegal value, thenINFO = -i.> 0β okay so this one's a whole vibe check on the computation itself, and it's giving "bitmask energy" π₯. Read it like this:- if
(MOD(INFO,2).NE.0), then one or more eigenvectors failed to converge π. Their indices are stored inIFAIL. EnsureABSTOL=2.0*PSLAMCH( 'U' ). Send e-mail toscalapack@cs.utk.edu - if
(MOD(INFO/2,2).NE.0), then eigenvectors corresponding to one or more clusters of eigenvalues could not be reorthogonalized because of insufficient workspace π. The indices of the clusters are stored in the arrayICLUSTR. - if
(MOD(INFO/4,2).NE.0), then space limit preventedPCHEEVXfrom computing all of the eigenvectors betweenVLandVUπ€. The number of eigenvectors computed is returned inNZ. - if
(MOD(INFO/8,2).NE.0), thenPCSTEBZfailed to compute eigenvalues π. EnsureABSTOL=2.0*PSLAMCH( 'U' ). Send e-mail toscalapack@cs.utk.edu
- if
ngl the< 0illegal-value thing is not the moment β if you see a negativeINFO, your input was lowkey cooked, so go check your arguments before you blame the routine, bestie π«Ά
okay and that's genuinely the whole page, we made it π
This one's from LAPACK version 1.5, dated 12 May 1997 β yes it's vintage, yes it still slays, and it's filed under PCHEEVX(l) in the manual pages. Go forth and compute your eigenvectors, my love, and may your INFO always be 0 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