SRC/dsyevd_2stage.f(3)
π NAME
okay so this is the Library Functions Manual page for SRC/dsyevd_2stage.f, and it's literally the home of the dsyevd_2stage subroutine. Bestie, get into it. β¨
β¨ SYNOPSIS β¨
π Functions/Subroutines π
subroutine dsyevd_2stage (jobz, uplo, n, a, lda, w, work, lwork, iwork, liwork, info)βDSYEVD_2STAGEcomputes the eigenvalues and, optionally, the left and/or right eigenvectors for SY matrices.
π«Ά Function/Subroutine Documentation π«Ά
subroutine dsyevd_2stage (character jobz, character uplo, integer n, double
precision, dimension( lda, * ) a, integer lda, double precision,
dimension( * ) w, double precision, dimension( * ) work, integer lwork,
integer, dimension( * ) iwork, integer liwork, integer info)
DSYEVD_2STAGE computes the eigenvalues and, optionally, the left and/or right eigenvectors for SY matrices.
π Purpose π
okay so DSYEVD_2STAGE computes all eigenvalues and, optionally, eigenvectors of a real symmetric matrix A using the 2stage technique for the reduction to tridiagonal. If eigenvectors are desired, it uses a divide and conquer algorithm. That's the whole purpose, bestie. π¦
π§ Parameters π§
JOBZβ isCHARACTER*1:= 'N': compute eigenvalues only;= 'V': compute eigenvalues and eigenvectors. Not available in this release.
UPLOβ isCHARACTER*1:= 'U': upper triangle ofAis stored;= 'L': lower triangle ofAis stored.
Nβ isINTEGER: the order of the matrixA.N >= 0.Aβ isDOUBLE PRECISION array, 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, ifJOBZ = 'V', then ifINFO = 0,Acontains the orthonormal eigenvectors of the matrixA. IfJOBZ = 'N', then on exit the lower triangle (ifUPLO='L') or the upper triangle (ifUPLO='U') ofA, including the diagonal, is destroyed.LDAβ isINTEGER: the leading dimension of the arrayA.LDA >= max(1,N).Wβ isDOUBLE PRECISION array, dimension (N). IfINFO = 0, the eigenvalues in ascending order.WORKβ isDOUBLE PRECISION array, dimension (LWORK). On exit, ifINFO = 0,WORK(1)returns the optimalLWORK.LWORKβ isINTEGER: the dimension of the arrayWORK.- If
N <= 1,LWORKmust be at least1. - If
JOBZ = 'N'andN > 1,LWORKmust be queried.
- If
LWORK = MAX(1, dimension) where
dimension = max(stage1,stage2) + (KD+1)*N + 2*N+1
= N*KD + N*max(KD+1,FACTOPTNB)
+ max(2*KD*KD, KD*NTHREADS)
+ (KD+1)*N + 2*N+1
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'andN > 1,LWORKmust be at least1 + 6*N + 2*N**2. - If
LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal sizes of theWORKandIWORKarrays, returns these values as the first entries of theWORKandIWORKarrays, and no error message related toLWORKorLIWORKis issued byXERBLA.
IWORKβ isINTEGER array, dimension (MAX(1,LIWORK)). On exit, ifINFO = 0,IWORK(1)returns the optimalLIWORK.LIWORKβ isINTEGER: the dimension of the arrayIWORK.- If
N <= 1,LIWORKmust be at least1. - If
JOBZ = 'N'andN > 1,LIWORKmust be at least1. - If
JOBZ = 'V'andN > 1,LIWORKmust be at least3 + 5*N. - If
LIWORK = -1, then a workspace query is assumed; the routine only calculates the optimal sizes of theWORKandIWORKarrays, returns these values as the first entries of theWORKandIWORKarrays, and no error message related toLWORKorLIWORKis issued byXERBLA.
- If
INFOβ isINTEGER:= 0: successful exit< 0: ifINFO = -i, thei-th argument had an illegal value> 0: ifINFO = iandJOBZ = 'N', then the algorithm failed to converge;ioff-diagonal elements of an intermediate tridiagonal form did not converge to zero; ifINFO = iandJOBZ = 'V', then the algorithm failed to compute an eigenvalue while working on the submatrix lying in rows and columnsINFO/(N+1)throughmod(INFO,N+1).
π Author π
- Univ. of Tennessee
- Univ. of California Berkeley
- Univ. of Colorado Denver
- NAG Ltd.
π Contributors π
- Jeff Rutter, Computer Science Division, University of California at Berkeley, USA
- Modified by Francoise Tisseur, University of Tennessee
- Modified description of INFO. Sven, 16 Feb 05.
π₯ Further Details π₯
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
Definition at line 219 of file dsyevd_2stage.f.
π Author π
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