elpa_impl_math_generalized_template.F90 File Reference

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Functions/Subroutines
subroutine	elpa_generalized_eigenvectors_a_h_a_ELPA_IMPL_SUFFIX (self, a, b, ev, q, is_already_decomposed, error)
	elpa_generalized_eigenvectors_a_h_a: class method to solve the eigenvalue problem, using host arrays
subroutine	elpa_generalized_eigenvectors_a_h_a_ELPA_IMPL_SUFFIX_c (handle, a_p, b_p, ev_p, q_p, is_already_decomposed, error)
subroutine	elpa_generalized_eigenvectors_d_ptr_ELPA_IMPL_SUFFIX (self, adev, bdev, evdev, qdev, is_already_decomposed, error)
	elpa_generalized_eigenvectors_d_ptr: class method to solve the eigenvalue problem, using device pointers
subroutine	elpa_generalized_eigenvectors_d_ptr_ELPA_IMPL_SUFFIX_c (handle, a_p, b_p, ev_p, q_p, is_already_decomposed, error)
subroutine	elpa_generalized_eigenvalues_a_h_a_ELPA_IMPL_SUFFIX (self, a, b, ev, is_already_decomposed, error)
	elpa_generalized_eigenvalues_a_h_a: class method to solve the eigenvalue problem, using host arrays
subroutine	elpa_generalized_eigenvalues_a_h_a_ELPA_IMPL_SUFFIX_c (handle, a_p, b_p, ev_p, is_already_decomposed, error)
subroutine	elpa_generalized_eigenvalues_d_ptr_ELPA_IMPL_SUFFIX (self, adev, bdev, evdev, is_already_decomposed, error)
	elpa_generalized_eigenvalues_d_ptr: class method to solve the eigenvalue problem, using device pointers
subroutine	elpa_generalized_eigenvalues_d_ptr_ELPA_IMPL_SUFFIX_c (handle, a_p, b_p, ev_p, is_already_decomposed, error)

Function/Subroutine Documentation

elpa_generalized_eigenvalues_a_h_a_ELPA_IMPL_SUFFIX()

subroutine elpa_generalized_eigenvalues_a_h_a_ELPA_IMPL_SUFFIX	(	class(elpa_impl_t)	self,
			a,
			b,
		real(kind=c_real_datatype), dimension(self%na)	ev,
		logical	is_already_decomposed,
		integer, optional	error )

elpa_generalized_eigenvalues_a_h_a: class method to solve the eigenvalue problem, using host arrays

The dimensions of the matrix a (locally ditributed and global), the block-cyclic distribution blocksize, the number of eigenvectors to be computed and the MPI communicators are already known to the object and MUST be set BEFORE with the class method "setup"

It is possible to change the behaviour of the method by setting tunable parameters with the class method "set"

Parameters

Parameters

a	Distributed matrix for which eigenvalues are to be computed. Distribution is like in Scalapack. The full matrix must be set (not only one half like in scalapack). Destroyed on exit (upper and lower half).
b	Distributed matrix, part of the generalized eigenvector problem, or the product of a previous call to this function (see is_already_decomposed). Distribution is like in Scalapack. If is_already_decomposed is false, on exit replaced by the decomposition
ev	On output: eigenvalues of a, every processor gets the complete set
is_already_decomposed	has to be set to .false. for the first call with a given b and .true. for each subsequent call with the same b, since b then already contains decomposition and thus the decomposing step is skipped
error	integer, optional: returns an error code, which can be queried with elpa_strerr

elpa_generalized_eigenvalues_a_h_a_ELPA_IMPL_SUFFIX_c()

subroutine elpa_generalized_eigenvalues_a_h_a_ELPA_IMPL_SUFFIX_c	(	value	handle,
		value	a_p,
		value	b_p,
		value	ev_p,
		integer(kind=c_int), intent(in), value	is_already_decomposed,
		integer(kind=c_int), intent(in)	error )

elpa_generalized_eigenvalues_d_ptr_ELPA_IMPL_SUFFIX()

subroutine elpa_generalized_eigenvalues_d_ptr_ELPA_IMPL_SUFFIX	(	class(elpa_impl_t)	self,
		type(c_ptr)	adev,
		type(c_ptr)	bdev,
		type(c_ptr)	evdev,
		logical	is_already_decomposed,
		integer, optional	error )

elpa_generalized_eigenvalues_d_ptr: class method to solve the eigenvalue problem, using device pointers

The dimensions of the matrix a (locally ditributed and global), the block-cyclic distribution blocksize, the number of eigenvectors to be computed and the MPI communicators are already known to the object and MUST be set BEFORE with the class method "setup"

It is possible to change the behaviour of the method by setting tunable parameters with the class method "set"

Parameters

Parameters

a	Distributed matrix for which eigenvalues are to be computed. Distribution is like in Scalapack. The full matrix must be set (not only one half like in scalapack). Destroyed on exit (upper and lower half).
b	Distributed matrix, part of the generalized eigenvector problem, or the product of a previous call to this function (see is_already_decomposed). Distribution is like in Scalapack. If is_already_decomposed is false, on exit replaced by the decomposition
ev	On output: eigenvalues of a, every processor gets the complete set
is_already_decomposed	has to be set to .false. for the first call with a given b and .true. for each subsequent call with the same b, since b then already contains decomposition and thus the decomposing step is skipped
error	integer, optional: returns an error code, which can be queried with elpa_strerr

elpa_generalized_eigenvalues_d_ptr_ELPA_IMPL_SUFFIX_c()

subroutine elpa_generalized_eigenvalues_d_ptr_ELPA_IMPL_SUFFIX_c	(	value	handle,
		value	a_p,
		value	b_p,
		value	ev_p,
		integer(kind=c_int), intent(in), value	is_already_decomposed,
		integer(kind=c_int), intent(in)	error )

elpa_generalized_eigenvectors_a_h_a_ELPA_IMPL_SUFFIX()

subroutine elpa_generalized_eigenvectors_a_h_a_ELPA_IMPL_SUFFIX	(	class(elpa_impl_t)	self,
			a,
			b,
		real(kind=c_real_datatype), dimension(self%na)	ev,
			q,
		logical	is_already_decomposed,
		integer, optional	error )

elpa_generalized_eigenvectors_a_h_a: class method to solve the eigenvalue problem, using host arrays

The dimensions of the matrix a (locally ditributed and global), the block-cyclic distribution blocksize, the number of eigenvectors to be computed and the MPI communicators are already known to the object and MUST be set BEFORE with the class method "setup"

It is possible to change the behaviour of the method by setting tunable parameters with the class method "set"

Parameters

Parameters

a	Distributed matrix for which eigenvalues are to be computed. Distribution is like in Scalapack. The full matrix must be set (not only one half like in scalapack). Destroyed on exit (upper and lower half).
b	Distributed matrix, part of the generalized eigenvector problem, or the product of a previous call to this function (see is_already_decomposed). Distribution is like in Scalapack. If is_already_decomposed is false, on exit replaced by the decomposition
ev	On output: eigenvalues of a, every processor gets the complete set
q	On output: Eigenvectors of a Distribution is like in Scalapack. Must be always dimensioned to the full size (corresponding to (na,na)) even if only a part of the eigenvalues is needed.
is_already_decomposed	has to be set to .false. for the first call with a given b and .true. for each subsequent call with the same b, since b then already contains decomposition and thus the decomposing step is skipped
error	integer, optional: returns an error code, which can be queried with elpa_strerr

elpa_generalized_eigenvectors_a_h_a_ELPA_IMPL_SUFFIX_c()

subroutine elpa_generalized_eigenvectors_a_h_a_ELPA_IMPL_SUFFIX_c	(	value	handle,
		value	a_p,
		value	b_p,
		value	ev_p,
		value	q_p,
		integer(kind=c_int), intent(in), value	is_already_decomposed,
		integer(kind=c_int), intent(in)	error )

elpa_generalized_eigenvectors_d_ptr_ELPA_IMPL_SUFFIX()

subroutine elpa_generalized_eigenvectors_d_ptr_ELPA_IMPL_SUFFIX	(	class(elpa_impl_t)	self,
		type(c_ptr)	adev,
		type(c_ptr)	bdev,
		type(c_ptr)	evdev,
		type(c_ptr)	qdev,
		logical	is_already_decomposed,
		integer, optional	error )

elpa_generalized_eigenvectors_d_ptr: class method to solve the eigenvalue problem, using device pointers

The dimensions of the matrix a (locally ditributed and global), the block-cyclic distribution blocksize, the number of eigenvectors to be computed and the MPI communicators are already known to the object and MUST be set BEFORE with the class method "setup"

It is possible to change the behaviour of the method by setting tunable parameters with the class method "set"

Parameters

Parameters

a	Distributed matrix for which eigenvalues are to be computed. Distribution is like in Scalapack. The full matrix must be set (not only one half like in scalapack). Destroyed on exit (upper and lower half).
b	Distributed matrix, part of the generalized eigenvector problem, or the product of a previous call to this function (see is_already_decomposed). Distribution is like in Scalapack. If is_already_decomposed is false, on exit replaced by the decomposition
ev	On output: eigenvalues of a, every processor gets the complete set
q	On output: Eigenvectors of a Distribution is like in Scalapack. Must be always dimensioned to the full size (corresponding to (na,na)) even if only a part of the eigenvalues is needed.
is_already_decomposed	has to be set to .false. for the first call with a given b and .true. for each subsequent call with the same b, since b then already contains decomposition and thus the decomposing step is skipped
error	integer, optional: returns an error code, which can be queried with elpa_strerr

elpa_generalized_eigenvectors_d_ptr_ELPA_IMPL_SUFFIX_c()

subroutine elpa_generalized_eigenvectors_d_ptr_ELPA_IMPL_SUFFIX_c	(	value	handle,
		value	a_p,
		value	b_p,
		value	ev_p,
		value	q_p,
		integer(kind=c_int), intent(in), value	is_already_decomposed,
		integer(kind=c_int), intent(in)	error )