/home/abui...x_2stage.f(3)
π NAME
Okay bestie, welcome to /home/abui...x_2stage.f(3) β this is a page from the Library Functions Manual. The NAME line points at this source file, no cap:
/home/abuild/rpmbuild/BUILD/lapack-static-3.12.0-build/lapack-3.12.0/SRC/ssyevx_2stage.f
π SYNOPSIS
π§ Functions/Subroutines
Hereβs the icon call signature, exactly as the page gives it:
subroutine SSYEVX_2STAGE (jobz, range, uplo, n, a, lda, vl, vu, il, iu,
abstol, m, w, z, ldz, work, lwork, iwork, ifail, info)
One-line vibe check: SSYEVX_2STAGE computes the eigenvalues and, optionally, the left and/or right eigenvectors for SY matrices. π«
π¦ Function/Subroutine Documentation
Full declaration, no cap:
subroutine SSYEVX_2STAGE (character jobz, character range, character uplo,
integer n, real, dimension( lda, * ) a, integer lda, real vl, real vu,
integer il, integer iu, real abstol, integer m, real, dimension( * ) w,
real, dimension( ldz, * ) z, integer ldz, real, dimension( * ) work,
integer lwork, integer, dimension( * ) iwork, integer, dimension( * )
ifail, integer info)
Officially, this is the same one-liner: SSYEVX_2STAGE computes the eigenvalues and, optionally, the left and/or right eigenvectors for SY matrices. Iconic. π
π― Purpose
SSYEVX_2STAGE computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix A using the 2stage technique for the reduction to tridiagonal. Eigenvalues and eigenvectors can be selected by specifying either a range of values or a range of indices for the desired eigenvalues. π¦β¨
π Parameters
JOBZβJOBZisCHARACTER*1.= 'N': Compute eigenvalues only;= 'V': Compute eigenvalues and eigenvectors. Not available in this release. πRANGEβRANGEisCHARACTER*1.= 'A': all eigenvalues will be found.= 'V': all eigenvalues in the half-open interval(VL,VU]will be found.= 'I': theIL-th throughIU-th eigenvalues will be found.UPLOβUPLOisCHARACTER*1.= 'U': Upper triangle ofAis stored;= 'L': Lower triangle ofAis stored.NβNisINTEGER. The order of the matrixA.N >= 0.AβAisREALarray, dimension(LDA, N). On entry, the symmetric matrixA. IfUPLO = 'U', the leadingN-by-Nupper triangular part ofAcontains the upper triangular part of the matrixA. IfUPLO = 'L', the leadingN-by-Nlower triangular part ofAcontains the lower triangular part of the matrixA. On exit, the lower triangle (ifUPLO='L') or the upper triangle (ifUPLO='U') ofA, including the diagonal, is destroyed. πLDAβLDAisINTEGER. The leading dimension of the arrayA.LDA >= max(1,N).VLβVLisREAL. IfRANGE='V', the lower bound of the interval to be searched for eigenvalues.VL < VU. Not referenced ifRANGE = 'A'or'I'.VUβVUisREAL. IfRANGE='V', the upper bound of the interval to be searched for eigenvalues.VL < VU. Not referenced ifRANGE = 'A'or'I'.ILβILisINTEGER. IfRANGE='I', the index of the smallest eigenvalue to be returned.1 <= IL <= IU <= N, ifN > 0;IL = 1andIU = 0ifN = 0. Not referenced ifRANGE = 'A'or'V'.IUβIUisINTEGER. IfRANGE='I', the index of the largest eigenvalue to be returned.1 <= IL <= IU <= N, ifN > 0;IL = 1andIU = 0ifN = 0. Not referenced ifRANGE = 'A'or'V'.ABSTOLβABSTOLisREAL. 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*|T| will be used in its place, where |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*SLAMCH('S'), not zero. If this routine returns with INFO>0, indicating that some eigenvectors did not converge, try setting ABSTOL to 2*SLAMCH('S').
See by Demmel and Kahan, LAPACK Working Note #3. π
MβMisINTEGER. The total number of eigenvalues found.0 <= M <= N. IfRANGE = 'A',M = N, and ifRANGE = 'I',M = IU-IL+1.WβWisREALarray, dimension(N). On normal exit, the firstMelements contain the selected eigenvalues in ascending order. πΈZβZisREALarray, dimension(LDZ, max(1,M)). IfJOBZ = 'V', then ifINFO = 0, the firstMcolumns ofZcontain the orthonormal eigenvectors of the matrixAcorresponding to the selected eigenvalues, with thei-th column ofZholding the eigenvector associated withW(i). 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. Note: the user must ensure that at leastmax(1,M)columns are supplied in the arrayZ; ifRANGE = 'V', the exact value ofMis not known in advance and an upper bound must be used. π«ΆLDZβLDZisINTEGER. The leading dimension of the arrayZ.LDZ >= 1, and ifJOBZ = 'V',LDZ >= max(1,N).WORKβWORKisREALarray, dimension(MAX(1,LWORK)). On exit, ifINFO = 0,WORK(1)returns the optimalLWORK.LWORKβLWORKisINTEGER. The length of the arrayWORK.LWORK >= 1, whenN <= 1; otherwise:
If JOBZ = 'N' and N > 1, LWORK must be queried.
LWORK = MAX(1, 8*N, dimension) where
dimension = max(stage1,stage2) + (KD+1)*N + 3*N
= N*KD + N*max(KD+1,FACTOPTNB)
+ max(2*KD*KD, KD*NTHREADS)
+ (KD+1)*N + 3*N
where KD is the blocking size of the reduction, FACTOPTNB is the blocking used by the QR or LQ algorithm, usually FACTOPTNB=128 is a good choice, NTHREADS is the number of threads used when openMP compilation is enabled, otherwise =1.
If JOBZ = 'V' and N > 1, LWORK must be queried. Not yet available. π
If LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the WORK array, returns this value as the first entry of the WORK array, and no error message related to LWORK is issued by XERBLA.
IWORKβIWORKisINTEGERarray, dimension(5*N).IFAILβIFAILisINTEGERarray, dimension(N). IfJOBZ = 'V', then ifINFO = 0, the firstMelements ofIFAILare zero. IfINFO > 0, thenIFAILcontains the indices of the eigenvectors that failed to converge. IfJOBZ = 'N', thenIFAILis not referenced.INFOβINFOisINTEGER.= 0: successful exit.< 0: ifINFO = -i, thei-th argument had an illegal value.> 0: ifINFO = i, thenieigenvectors failed to converge. Their indices are stored in arrayIFAIL. π
π Author
- Univ. of Tennessee
- Univ. of California Berkeley
- Univ. of Colorado Denver
- NAG Ltd.
β¨ Further Details
All details about the 2stage techniques are available in:
- Azzam Haidar, Hatem Ltaief, and Jack Dongarra. Parallel reduction to condensed forms for symmetric eigenvalue problems using aggregated fine-grained and memory-aware kernels. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC '11), New York, NY, USA, Article 8 , 11 pages.
http://doi.acm.org/10.1145/2063384.2063394 - A. Haidar, J. Kurzak, P. Luszczek, 2013. An improved parallel singular value algorithm and its implementation for multicore hardware, In Proceedings of 2013 International Conference for High Performance Computing, Networking, Storage and Analysis (SC '13). Denver, Colorado, USA, 2013. Article 90, 12 pages.
http://doi.acm.org/10.1145/2503210.2503292 - A. Haidar, R. Solca, S. Tomov, T. Schulthess and J. Dongarra. A novel hybrid CPU-GPU generalized eigensolver for electronic structure calculations based on fine-grained memory aware tasks. International Journal of High Performance Computing Applications. Volume 28 Issue 2, Pages 196-209, May 2014.
http://hpc.sagepub.com/content/28/2/196
π Definition at line 297 of file ssyevx_2stage.f.
π Author
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