rocsolver_cheevd_strided_batched Interface Reference

rocsolver_cheevd_strided_batched Interface Reference#

HIPFORT API Reference: hipfort_rocsolver::rocsolver_cheevd_strided_batched Interface Reference
hipfort_rocsolver::rocsolver_cheevd_strided_batched Interface Reference

HEEVD_STRIDED_BATCHED computes the eigenvalues and optionally the eigenvectors of a batch of Hermitian matrices A_j. More...

Public Member Functions

integer(kind(rocblas_status_success)) function rocsolver_cheevd_strided_batched_ (handle, evect, uplo, n, A, lda, strideA, D, strideD, E, strideE, myInfo, batch_count)
 
integer(kind(rocblas_status_success)) function rocsolver_cheevd_strided_batched_full_rank (handle, evect, uplo, n, A, lda, strideA, D, strideD, E, strideE, myInfo, batch_count)
 
integer(kind(rocblas_status_success)) function rocsolver_cheevd_strided_batched_rank_0 (handle, evect, uplo, n, A, lda, strideA, D, strideD, E, strideE, myInfo, batch_count)
 
integer(kind(rocblas_status_success)) function rocsolver_cheevd_strided_batched_rank_1 (handle, evect, uplo, n, A, lda, strideA, D, strideD, E, strideE, myInfo, batch_count)
 

Detailed Description

HEEVD_STRIDED_BATCHED computes the eigenvalues and optionally the eigenvectors of a batch of Hermitian matrices A_j.

The eigenvalues are returned in ascending order. The eigenvectors are computed using a divide-and-conquer algorithm, depending on the value of evect. The computed eigenvectors are orthonormal.

Parameters
[in]handlerocblas_handle.
[in]evectrocblas_evect.
Specifies whether the eigenvectors are to be computed. If evect is rocblas_evect_original, then the eigenvectors are computed. rocblas_evect_tridiagonal is not supported.
[in]uplorocblas_fill.
Specifies whether the upper or lower part of the Hermitian matrices A_j is stored. If uplo indicates lower (or upper), then the upper (or lower) part of A_j is not used.
[in]nrocblas_int. n >= 0.
Number of rows and columns of matrices A_j.
[in,out]Apointer to type. Array on the GPU (the size depends on the value of strideA).
On entry, the matrices A_j. On exit, the eigenvectors of A_j if they were computed and the algorithm converged; otherwise the contents of A_j are destroyed.
[in]ldarocblas_int. lda >= n.
Specifies the leading dimension of matrices A_j.
[in]strideArocblas_stride.
Stride from the start of one matrix A_j to the next one A_(j+1). There is no restriction for the value of strideA. Normal use case is strideA >= lda*n.
[out]Dpointer to real type. Array on the GPU (the size depends on the value of strideD).
The eigenvalues of A_j in increasing order.
[in]strideDrocblas_stride.
Stride from the start of one vector D_j to the next one D_(j+1). There is no restriction for the value of strideD. Normal use case is strideD >= n.
[out]Epointer to real type. Array on the GPU (the size depends on the value of strideE).
This array is used to work internally with the tridiagonal matrix T_j associated with A_j. On exit, if info[j] > 0, E_j contains the unconverged off-diagonal elements of T_j (or properly speaking, a tridiagonal matrix equivalent to T_j). The diagonal elements of this matrix are in D_j; those that converged correspond to a subset of the eigenvalues of A_j (not necessarily ordered).
[in]strideErocblas_stride.
Stride from the start of one vector E_j to the next one E_(j+1). There is no restriction for the value of strideE. Normal use case is strideE >= n.
[out]infopointer to rocblas_int. Array of batch_count integers on the GPU.
If info[j] = 0, successful exit for matrix A_j. If info[j] = i > 0 and evect is rocblas_evect_none, the algorithm did not converge. i elements of E_j did not converge to zero. If info[j] = i > 0 and evect is rocblas_evect_original, the algorithm failed to compute an eigenvalue in the submatrix from [i/(n+1), i/(n+1)] to [i%(n+1), i%(n+1)].
[in]batch_countrocblas_int. batch_count >= 0.
Number of matrices in the batch.

Member Function/Subroutine Documentation

◆ rocsolver_cheevd_strided_batched_()

integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_cheevd_strided_batched::rocsolver_cheevd_strided_batched_ ( type(c_ptr), value  handle,
integer(kind(rocblas_evect_original)), value  evect,
integer(kind(rocblas_fill_upper)), value  uplo,
integer(c_int), value  n,
type(c_ptr), value  A,
integer(c_int), value  lda,
integer(c_int64_t), value  strideA,
type(c_ptr), value  D,
integer(c_int64_t), value  strideD,
type(c_ptr), value  E,
integer(c_int64_t), value  strideE,
integer(c_int)  myInfo,
integer(c_int), value  batch_count 
)

◆ rocsolver_cheevd_strided_batched_full_rank()

integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_cheevd_strided_batched::rocsolver_cheevd_strided_batched_full_rank ( type(c_ptr)  handle,
integer(kind(rocblas_evect_original))  evect,
integer(kind(rocblas_fill_upper))  uplo,
integer(c_int)  n,
complex(c_float_complex), dimension(:,:), target  A,
integer(c_int)  lda,
integer(c_int64_t)  strideA,
real(c_float), dimension(:), target  D,
integer(c_int64_t)  strideD,
real(c_float), dimension(:), target  E,
integer(c_int64_t)  strideE,
integer(c_int)  myInfo,
integer(c_int)  batch_count 
)

◆ rocsolver_cheevd_strided_batched_rank_0()

integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_cheevd_strided_batched::rocsolver_cheevd_strided_batched_rank_0 ( type(c_ptr)  handle,
integer(kind(rocblas_evect_original))  evect,
integer(kind(rocblas_fill_upper))  uplo,
integer(c_int)  n,
complex(c_float_complex), target  A,
integer(c_int)  lda,
integer(c_int64_t)  strideA,
real(c_float), target  D,
integer(c_int64_t)  strideD,
real(c_float), target  E,
integer(c_int64_t)  strideE,
integer(c_int)  myInfo,
integer(c_int)  batch_count 
)

◆ rocsolver_cheevd_strided_batched_rank_1()

integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_cheevd_strided_batched::rocsolver_cheevd_strided_batched_rank_1 ( type(c_ptr)  handle,
integer(kind(rocblas_evect_original))  evect,
integer(kind(rocblas_fill_upper))  uplo,
integer(c_int)  n,
complex(c_float_complex), dimension(:), target  A,
integer(c_int)  lda,
integer(c_int64_t)  strideA,
real(c_float), dimension(:), target  D,
integer(c_int64_t)  strideD,
real(c_float), dimension(:), target  E,
integer(c_int64_t)  strideE,
integer(c_int)  myInfo,
integer(c_int)  batch_count 
)

The documentation for this interface was generated from the following file: