DSYGV_2STAGE(3)
π DSYGV_2STAGE(3) β the whole entire manual, bestie π
Okay so, this page is from the Library Functions Manual, and it's giving serious LAPACK energy. Buckle up, we're going through it together. π«Άβ¨
π NAME π
/home/abuild/rpmbuild/BUILD/lapack-static-3.12.0-build/lapack-3.12.0/SRC/dsygv_2stage.f
That's the name. It's a whole path. It ate. π
π¦ SYNOPSIS π¦
β¨ Functions/Subroutines β¨
subroutine DSYGV_2STAGE (itype, jobz, uplo, n, a, lda, b, ldb, w, work,
lwork, info)
DSYGV_2STAGE
πΈ Function/Subroutine Documentation πΈ
subroutine DSYGV_2STAGE (integer itype, character jobz, character uplo,
integer n, double precision, dimension( lda, * ) a, integer lda, double
precision, dimension( ldb, * ) b, integer ldb, double precision,
dimension( * ) w, double precision, dimension( * ) work, integer lwork,
integer info)
DSYGV_2STAGE
π Purpose π
Okay so literally, DSYGV_2STAGE computes all the eigenvalues, and optionally the eigenvectors, of a real generalized symmetric-definite eigenproblem. The forms are:
A*x=(lambda)*B*xA*Bx=(lambda)*xB*A*x=(lambda)*x
Here A and B are assumed to be symmetric, and B is also positive definite. Periodt. π
This routine uses the 2stage technique for the reduction to tridiagonal, which β no cap β showed higher performance on recent architecture and for large sizes N>2000. It's giving optimized. π₯
π Parameters π
ITYPEβITYPEisINTEGER. 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
JOBZβJOBZisCHARACTER*1:= 'N': Compute eigenvalues only;= 'V': Compute eigenvalues and eigenvectors. Not available in this release. π
UPLOβUPLOisCHARACTER*1:= 'U': Upper triangles ofAandBare stored;= 'L': Lower triangles ofAandBare stored.
NβNisINTEGER. The order of the matricesAandB.N >= 0.AβAis aDOUBLE PRECISIONarray, dimension(LDA, N).- On entry, the symmetric matrix
A. 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, if
JOBZ = 'V', then ifINFO = 0,Acontains the matrixZof eigenvectors. The eigenvectors are normalized as follows: ifITYPE = 1 or 2,Z**T*B*Z = I; ifITYPE = 3,Z**T*inv(B)*Z = I. IfJOBZ = 'N', then on exit the upper triangle (ifUPLO='U') or the lower triangle (ifUPLO='L') ofA, including the diagonal, is destroyed. π
- On entry, the symmetric matrix
LDAβLDAisINTEGER. The leading dimension of the arrayA.LDA >= max(1,N).BβBis aDOUBLE PRECISIONarray, dimension(LDB, N).- On entry, the symmetric positive definite matrix
B. IfUPLO = 'U', the leadingN-by-Nupper triangular part ofBcontains the upper triangular part of the matrixB. IfUPLO = 'L', the leadingN-by-Nlower triangular part ofBcontains the lower triangular part of the matrixB. - On exit, if
INFO <= N, the part ofBcontaining the matrix is overwritten by the triangular factorUorLfrom the Cholesky factorizationB = U**T*UorB = L*L**T.
- On entry, the symmetric positive definite matrix
LDBβLDBisINTEGER. The leading dimension of the arrayB.LDB >= max(1,N).WβWis aDOUBLE PRECISIONarray, dimension(N). IfINFO = 0, the eigenvalues in ascending order.WORKβWORKis aDOUBLE PRECISIONarray, 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'andN > 1,LWORKmust be queried.
- If
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 β usually FACTOPTNB=128 is a good choice β and NTHREADS is the number of threads used when openMP compilation is enabled, otherwise =1.
- If
JOBZ = 'V'andN > 1,LWORKmust be queried. Not yet available. - If
LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of theWORKarray, returns this value as the first entry of theWORKarray, and no error message related toLWORKis issued byXERBLA.
INFOβINFOisINTEGER.= 0: successful exit< 0: ifINFO = -i, thei-th argument had an illegal value> 0:DPOTRForDSYEVreturned an error code:<= N: ifINFO = i,DSYEVfailed to converge;ioff-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 orderiofBis not positive. The factorization ofBcould not be completed and no eigenvalues or eigenvectors were computed. π«
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 dsygv_2stage.f.
π Author π
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