SRC/ssygv_2stage.f(3)
π NAME π
okay so this is the Library Functions Manual page for SRC/ssygv_2stage.f(3) β and yes bestie, the main character is the ssygv_2stage subroutine. This is literally the docs for SSYGV_2STAGE, no cap.
β¨ SYNOPSIS β¨
π Functions/Subroutines π
subroutine ssygv_2stage (itype, jobz, uplo, n, a, lda, b, ldb, w, work, lwork, info)βSSYGV_2STAGE
π¦ Function/Subroutine Documentation π¦
subroutine ssygv_2stage (integer itype, character jobz, character uplo,
integer n, real, dimension( lda, * ) a, integer lda, real, dimension(
ldb, * ) b, integer ldb, real, dimension( * ) w, real, dimension( * )
work, integer lwork, integer info)
SSYGV_2STAGE β that's the icon right there π
πΈ Purpose πΈ
okay so SSYGV_2STAGE computes all the eigenvalues, and optionally the eigenvectors, of a real generalized symmetric-definite eigenproblem, of the form A*x=(lambda)*B*x, A*Bx=(lambda)*x, or B*A*x=(lambda)*x. Here A and B are assumed to be symmetric, and B is also positive definite. This routine uses the 2stage technique for the reduction to tridiagonal, which showed higher performance on recent architecture and for large sizes N>2000 β¨
π Parameters π
ITYPEβITYPE is INTEGER. Specifies the problem type to be solved:= 1:A*x = (lambda)*B*x= 2:A*B*x = (lambda)*x= 3:B*A*x = (lambda)*x
JOBZβJOBZ is CHARACTER*1= 'N':Compute eigenvalues only;= 'V':Compute eigenvalues and eigenvectors. Not available in this release.
UPLOβUPLO is CHARACTER*1= 'U':Upper triangles of A and B are stored;= 'L':Lower triangles of A and B are stored.
NβN is INTEGER. The order of the matrices A and B.N >= 0.AβA is REAL array, dimension (LDA, N). On entry, the symmetric matrix A. IfUPLO = 'U', the leading N-by-N upper triangular part of A contains the upper triangular part of the matrix A. IfUPLO = 'L', the leading N-by-N lower triangular part of A contains the lower triangular part of the matrix A.
On exit, if JOBZ = 'V', then if INFO = 0, A contains the matrix Z of eigenvectors. The eigenvectors are normalized as follows: if ITYPE = 1 or 2, Z**T*B*Z = I; if ITYPE = 3, Z**T*inv(B)*Z = I. If JOBZ = 'N', then on exit the upper triangle (if UPLO='U') or the lower triangle (if UPLO='L') of A, including the diagonal, is destroyed π
LDAβLDA is INTEGER. The leading dimension of the array A.LDA >= max(1,N).BβB is REAL array, dimension (LDB, N). On entry, the symmetric positive definite matrix B. IfUPLO = 'U', the leading N-by-N upper triangular part of B contains the upper triangular part of the matrix B. IfUPLO = 'L', the leading N-by-N lower triangular part of B contains the lower triangular part of the matrix B.
On exit, if INFO <= N, the part of B containing the matrix is overwritten by the triangular factor U or L from the Cholesky factorization B = U**T*U or B = L*L**T.
LDBβLDB is INTEGER. The leading dimension of the array B.LDB >= max(1,N).WβW is REAL array, dimension (N). IfINFO = 0, the eigenvalues in ascending order.WORKβWORK is REAL array, dimension (MAX(1,LWORK)). On exit, ifINFO = 0,WORK(1)returns the optimalLWORK.LWORKβLWORK is INTEGER. The length of the arrayWORK.LWORK >= 1, whenN <= 1; otherwise:
If JOBZ = 'N' and N > 1, LWORK must be queried.
LWORK = MAX(1, dimension) where
dimension = max(stage1,stage2) + (KD+1)*N + 2*N
= N*KD + N*max(KD+1,FACTOPTNB)
+ max(2*KD*KD, KD*NTHREADS)
+ (KD+1)*N + 2*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.
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.
INFOβINFO is INTEGER= 0:successful exit< 0:ifINFO = -i, the i-th argument had an illegal value> 0:SPOTRForSSYEVreturned an error code:<= N:ifINFO = i,SSYEVfailed to converge; i off-diagonal elements of an intermediate tridiagonal form did not converge to zero;> N:ifINFO = N + i, for1 <= i <= N, then the leading principal minor of order i of B is not positive. The factorization of B could not be completed and no eigenvalues or eigenvectors were computed.
π Author π
- Univ. of Tennessee
- Univ. of California Berkeley
- Univ. of Colorado Denver
- NAG Ltd.
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 224 of file ssygv_2stage.f.
π§ Author π§
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