PSSYEVX(l)
π NAME π
Okay bestie, listen up β this is PSSYEVX(l), a LAPACK routine (version 1.5), and she is that girl. β¨
PSSYEVX β computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix A by calling the recommended sequence of ScaLAPACK routines. π¦
π SYNOPSIS π
No cap, here's the whole signature, verbatim, don't touch a single character:
SUBROUTINE PSSYEVX( JOBZ, RANGE, UPLO, N, A, IA, JA, DESCA, VL, VU, IL,
IU, ABSTOL, M, NZ, W, ORFAC, Z, IZ, JZ, DESCZ, WORK,
LWORK, IWORK, LIWORK, IFAIL, ICLUSTR, GAP, INFO )
CHARACTER JOBZ, RANGE, UPLO
INTEGER IA, IL, INFO, IU, IZ, JA, JZ, LIWORK, LWORK, M, N,
NZ
REAL ABSTOL, ORFAC, VL, VU
INTEGER DESCA( * ), DESCZ( * ), ICLUSTR( * ), IFAIL( * ),
IWORK( * )
REAL A( * ), GAP( * ), W( * ), 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, INDTAU, INDWORK,
IROFFA, IROFFZ, ISCALE, ISIZESTEBZ, ISIZESTEIN,
IZROW, LALLWORK, LIWMIN, LLWORK, LWMIN, MAXEIGS,
MB_A, MB_Z, MQ0, MYCOL, MYROW, NB, NB_A, NB_Z, NEIG,
NN, NNP, NP0, NPCOL, NPROCS, NPROW, NSPLIT, NZZ,
OFFSET, RSRC_A, RSRC_Z, SIZEORMTR, SIZESTEIN,
SIZESYEVX
REAL ABSTLL, ANRM, BIGNUM, EPS, RMAX, RMIN, SAFMIN,
SIGMA, SMLNUM, VLL, VUU
INTEGER IDUM1( 4 ), IDUM2( 4 )
LOGICAL LSAME
INTEGER ICEIL, INDXG2P, NUMROC
REAL PSLAMCH, PSLANSY
EXTERNAL LSAME, ICEIL, INDXG2P, NUMROC, PSLAMCH, PSLANSY
EXTERNAL BLACS_GRIDINFO, CHK1MAT, IGAMN2D, PCHK2MAT, PSELGET,
PSLARED1D, PSLASCL, PSORMTR, PSSTEBZ, PSSTEIN,
PSSYTRD, PXERBLA, SGEBR2D, SGEBS2D, SLASRT, SSCAL
INTRINSIC ABS, ICHAR, MAX, MIN, MOD, REAL, SQRT
π PURPOSE π
PSSYEVX computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix A by calling the recommended sequence of ScaLAPACK routines. Eigenvalues/vectors can be selected by specifying a range of values or a range of indices for the desired eigenvalues. π
π NOTES π
Okay so, 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. β¨
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«Ά
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 grid A is 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
PSSYEVX assumes IEEE 754 standard compliant arithmetic. 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§
π ARGUMENTS π
NP = the number of rows local to a given process.NQ = the number of columns local to a given process.
JOBZ(global input)CHARACTER*1β specifies whether or not to compute the eigenvectors:= 'N': Compute eigenvalues only.= 'V': Compute eigenvalues and eigenvectors.
RANGE(global input)CHARACTER*1β= '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(global input)CHARACTER*1β specifies whether the upper or lower triangular part of the symmetric matrixAis stored:= 'U': Upper triangular= 'L': Lower triangular
N(global input)INTEGERβ the number of rows and columns of the matrixA.N >= 0.A(local input/workspace) block cyclicREALarray, global dimension(N, N), local dimension( LLD_A, LOCc(JA+N-1) )- On entry, the symmetric matrix
A. IfUPLO = 'U', only the upper triangular part ofAis used to define the elements of the symmetric matrix. IfUPLO = 'L', only the lower triangular part ofAis used to define the elements of the symmetric matrix. - On exit, the lower triangle (if
UPLO='L') or the upper triangle (ifUPLO='U') ofA, including the diagonal, is destroyed. π
- On entry, the symmetric matrix
IA(global input)INTEGERβA's global row index, which points to the beginning of the submatrix which is to be operated on.JA(global input)INTEGERβA's global column index, which points to the beginning of the submatrix which is to be operated on.DESCA(global and local input)INTEGERarray of dimensionDLEN_. The array descriptor for the distributed matrixA. IfDESCA( CTXT_ )is incorrect,PSSYEVXcannot guarantee correct error reporting. πVL(global input)REALβ ifRANGE='V', the lower bound of the interval to be searched for eigenvalues. Not referenced ifRANGE = 'A'or'I'.VU(global input)REALβ ifRANGE='V', the upper bound of the interval to be searched for eigenvalues. Not referenced ifRANGE = 'A'or'I'.IL(global input)INTEGERβ ifRANGE='I', the index (from smallest to largest) of the smallest eigenvalue to be returned.IL >= 1. Not referenced ifRANGE = 'A'or'V'.IU(global input)INTEGERβ ifRANGE='I', the index (from smallest to largest) of the largest eigenvalue to be returned.min(IL,N) <= IU <= N. Not referenced ifRANGE = 'A'or'V'.ABSTOL(global input)REAL- If
JOBZ='V', settingABSTOLtoPSLAMCH( 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
- If
ABSTOL + EPS * max( |a|,|b| ) ,
- where
EPSis the machine precision. IfABSTOLis less than or equal to zero, thenEPS*norm(T)will be used in its place, wherenorm(T)is the 1-norm of the tridiagonal matrix obtained by reducingAto tridiagonal form. - Eigenvalues will be computed most accurately when
ABSTOLis set to twice the underflow threshold2*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 settingABSTOLto2*PSLAMCH('S'). - 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(global output)INTEGERβ total number of eigenvalues found.0 <= M <= N.NZ(global output)INTEGERβ total number of eigenvectors computed.0 <= NZ <= M. The number of columns ofZthat are filled. IfJOBZ .NE. 'V',NZis not referenced. IfJOBZ .EQ. 'V',NZ = Munless the user supplies insufficient space andPSSYEVXis 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.)PSSYEVXis always able to detect insufficient space without computation unlessRANGE .EQ. 'V'. π₯W(global output)REAL array, dimension (N)β okay so on a normal exit, the firstMentries contain the selected eigenvalues in ascending order. periodt. β¨ORFAC(global input)REALβ bestie, this one decides which eigenvectors should be reorthogonalized. Eigenvectors that correspond to eigenvalues which are withintol=ORFAC*norm(A)of each other are to be reorthogonalized. But waitβ 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. no cap.Z(local output)REAL 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. slay.IZ(global input)INTEGERβZ's global row index, which points to the beginning of the submatrix which is to be operated on. lowkey important.JZ(global input)INTEGERβZ's global column index, which points to the beginning of the submatrix which is to be operated on. same vibe.DESCZ(global and local input)INTEGER array of dimension DLEN_.β The array descriptor for the distributed matrixZ.DESCZ( CTXT_ )must equalDESCA( CTXT_ ). don't mess this up.WORK(local workspace/output)REAL array, dimension(LWORK)β On return,WROK(1)contains the optimal amount of workspace required for efficient execution. okay so:- if
JOBZ='N'WORK(1)= optimal amount of workspace required to compute eigenvalues efficiently - if
JOBZ='V'WORK(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.
- if
LWORK(local input)INTEGERβ Size ofWORK. See below for definitions of variables used to defineLWORK.- If no eigenvectors are requested (
JOBZ = 'N') then
- If no eigenvectors are requested (
LWORK >= 5 * N + MAX( 5 * NN, NB * ( NP0 + 1 ) )
- If eigenvectors are requested (
JOBZ = 'V') then the amount of workspace required to guarantee that all eigenvectors are computed is:
LWORK >= 5*N + MAX( 5*NN, NP0 * MQ0 + 2 * NB * NB ) +
ICEIL( NEIG, NPROW*NPCOL)*NN
- The computed eigenvectors may not be orthogonal if the minimal workspace is supplied and
ORFACis too small. If you want to guarantee orthogonality (at the cost of potentially poor performance) you should add the following toLWORK:
(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 requestedNB = DESCA( MB_ ) = DESCA( NB_ ) = DESCZ( MB_ ) = DESCZ( NB_ )NN = MAX( N, NB, 2 )DESCA( RSRC_ ) = DESCA( NB_ ) = DESCZ( RSRC_ ) = DESCZ( CSRC_ ) = 0NP0 = NUMROC( NN, NB, 0, 0, NPROW )MQ0 = NUMROC( MAX( NEIG, NB, 2 ), NB, 0, 0, NPCOL )ICEIL( X, Y )is a ScaLAPACK function returningceiling(X/Y)
- When
LWORKis too small:- If
LWORKis too small to guarantee orthogonality,PSSYEVXattempts to maintain orthogonality in the clusters with the smallest spacing between the eigenvalues. - If
LWORKis too small to compute all the eigenvectors requested, no computation is performed andINFO=-23is returned. Note that whenRANGE='V',PSSYEVXdoes not know how many eigenvectors are requested until the eigenvalues are computed. Therefore, whenRANGE='V'and as long asLWORKis large enough to allowPSSYEVXto compute the eigenvalues,PSSYEVXwill compute the eigenvalues and as many eigenvectors as it can.
- If
- Relationship between workspace, orthogonality & performance:
- Greater performance can be achieved if adequate workspace is provided. On the other hand, in some situations, performance can decrease as the workspace provided increases above the workspace amount shown below:
- For optimal performance, greater workspace may be needed, i.e.
LWORK >= MAX( LWORK, 5*N + NSYTRD_LWOPT )
Where:
LWORK, as defined previously, depends upon the number of eigenvectors requested, and- ``` NSYTRD_LWOPT = N + 2*( ANB+1 )*( 4*NPS+2 ) + ( NPS + 3 ) * NPS
- ```
ANB = PJLAENV( DESCA( CTXT_), 3, 'PSSYTTRD', 'L',
0, 0, 0, 0)
- ``` SQNPC = INT( SQRT( DBLE( NPROW * NPCOL ) ) )
- ```
NPS = MAX( NUMROC( N, 1, 0, 0, SQNPC ), 2*ANB )
NUMROCis a ScaLAPACK tool functions;PJLAENVis a ScaLAPACK envionmental inquiry functionMYROW,MYCOL,NPROWandNPCOLcan be determined by calling the subroutineBLACS_GRIDINFO.
- For large
N, no extra workspace is needed, however the biggest boost in performance comes for smallN, so it is wise to provide the extra workspace (typically less than a Megabyte per process). - 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)PSSTEINwill perform no better thanSSTEINon 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
LWORK = -1, thenLWORKis 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 byPXERBLA.
IWORK(local workspace)INTEGER arrayβ On return,IWORK(1)contains the amount of integer workspace required.LIWORK(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.
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. got it? 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'. zero terminated, bestie.GAP(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. typo preserved, no cap.INFO(global output)INTEGER= 0: successful exit< 0: 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.> 0:- if
(MOD(INFO,2).NE.0), then one or more eigenvectors failed to converge. 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. The indices of the clusters are stored in the arrayICLUSTR. - if
(MOD(INFO/4,2).NE.0), then space limit preventedPSSYEVXfrom computing all of the eigenvectors betweenVLandVU. The number of eigenvectors computed is returned inNZ. - if
(MOD(INFO/8,2).NE.0), thenPSSTEBZfailed to compute eigenvalues. EnsureABSTOL=2.0*PSLAMCH( 'U' )Send e-mail toscalapack@cs.utk.edu
- if
This page is from LAPACK version 1.5, dated 12 May 1997, and it's the PSSYEVX(l) manpage. Stay iconic, bestie! πβ¨
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