rocsolver_ssyevd_strided_batched Interface Reference#
hipfort_rocsolver::rocsolver_ssyevd_strided_batched Interface Reference
SYEVD_STRIDED_BATCHED computes the eigenvalues and optionally the eigenvectors of a batch of real symmetric matrices A_j. More...
Public Member Functions | |
integer(kind(rocblas_status_success)) function | rocsolver_ssyevd_strided_batched_ (handle, evect, uplo, n, A, lda, strideA, D, strideD, E, strideE, myInfo, batch_count) |
integer(kind(rocblas_status_success)) function | rocsolver_ssyevd_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_ssyevd_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_ssyevd_strided_batched_rank_1 (handle, evect, uplo, n, A, lda, strideA, D, strideD, E, strideE, myInfo, batch_count) |
Detailed Description
SYEVD_STRIDED_BATCHED computes the eigenvalues and optionally the eigenvectors of a batch of real symmetric 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] handle rocblas_handle. [in] evect rocblas_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] uplo rocblas_fill.
Specifies whether the upper or lower part of the symmetric matrices A_j is stored. If uplo indicates lower (or upper), then the upper (or lower) part of A_j is not used.[in] n rocblas_int. n >= 0.
Number of rows and columns of matrices A_j.[in,out] A pointer 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] lda rocblas_int. lda >= n.
Specifies the leading dimension of matrices A_j.[in] strideA rocblas_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] D pointer to type. Array on the GPU (the size depends on the value of strideD).
The eigenvalues of A_j in increasing order.[in] strideD rocblas_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] E pointer to 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] strideE rocblas_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] info pointer 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_count rocblas_int. batch_count >= 0.
Number of matrices in the batch.
Member Function/Subroutine Documentation
◆ rocsolver_ssyevd_strided_batched_()
integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_ssyevd_strided_batched::rocsolver_ssyevd_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_ssyevd_strided_batched_full_rank()
integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_ssyevd_strided_batched::rocsolver_ssyevd_strided_batched_full_rank | ( | type(c_ptr) | handle, |
integer(kind(rocblas_evect_original)) | evect, | ||
integer(kind(rocblas_fill_upper)) | uplo, | ||
integer(c_int) | n, | ||
real(c_float), 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_ssyevd_strided_batched_rank_0()
integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_ssyevd_strided_batched::rocsolver_ssyevd_strided_batched_rank_0 | ( | type(c_ptr) | handle, |
integer(kind(rocblas_evect_original)) | evect, | ||
integer(kind(rocblas_fill_upper)) | uplo, | ||
integer(c_int) | n, | ||
real(c_float), 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_ssyevd_strided_batched_rank_1()
integer(kind(rocblas_status_success)) function hipfort_rocsolver::rocsolver_ssyevd_strided_batched::rocsolver_ssyevd_strided_batched_rank_1 | ( | type(c_ptr) | handle, |
integer(kind(rocblas_evect_original)) | evect, | ||
integer(kind(rocblas_fill_upper)) | uplo, | ||
integer(c_int) | n, | ||
real(c_float), 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: