elpa_impl_math_template.F90 File Reference

Functions/Subroutines
subroutine	elpa_eigenvectors_ELPA_IMPL_SUFFIX (self, a, ev, q, error)
	elpa_eigenvectors_d: class method to solve the eigenvalue problem More...
subroutine	elpa_eigenvectors_ELPA_IMPL_SUFFIX_c (handle, a_p, ev_p, q_p, error)
subroutine	elpa_eigenvalues_ELPA_IMPL_SUFFIX (self, a, ev, error)
	elpa_eigenvalues_d: class method to solve the eigenvalue problem More...
subroutine	elpa_eigenvalues_ELPA_IMPL_SUFFIX_c (handle, a_p, ev_p, error)
subroutine	elpa_generalized_eigenvectors_ELPA_IMPL_SUFFIX (self, a, b, ev, q, is_already_decomposed, error)
	elpa_generalized_eigenvectors_d: class method to solve the eigenvalue problem More...
subroutine	elpa_generalized_eigenvectors_ELPA_IMPL_SUFFIX_c (handle, a_p, b_p, ev_p, q_p, is_already_decomposed, error)
subroutine	elpa_generalized_eigenvalues_ELPA_IMPL_SUFFIX (self, a, b, ev, is_already_decomposed, error)
	elpa_generalized_eigenvalues_d: class method to solve the eigenvalue problem More...
subroutine	elpa_generalized_eigenvalues_ELPA_IMPL_SUFFIX_c (handle, a_p, b_p, ev_p, is_already_decomposed, error)
subroutine	elpa_hermitian_multiply_ELPA_IMPL_SUFFIX (self, uplo_a, uplo_c, ncb, a, b, nrows_b, ncols_b, c, nrows_c, ncols_c, error)
	elpa_hermitian_multiply_d: class method to perform C : = A**T * B where A is a square matrix (selfna,selfna) which is optionally upper or lower triangular B is a (selfna,ncb) matrix C is a (selfna,ncb) matrix where optionally only the upper or lower triangle may be computed More...
subroutine	elpa_hermitian_multiply_ELPA_IMPL_SUFFIX_c (handle, uplo_a, uplo_c, ncb, a_p, b, nrows_b, ncols_b, c, nrows_c, ncols_c, error)
subroutine	elpa_cholesky_ELPA_IMPL_SUFFIX (self, a, error)
	elpa_choleksy_d: class method to do a cholesky factorization More...
subroutine	elpa_choleksy_ELPA_IMPL_SUFFIX_c (handle, a_p, error)
subroutine	elpa_invert_trm_ELPA_IMPL_SUFFIX (self, a, error)
	elpa_invert_trm_d: class method to invert a triangular More...
subroutine	elpa_invert_trm_ELPA_IMPL_SUFFIX_c (handle, a_p, error)
subroutine	elpa_solve_tridiagonal_ELPA_IMPL_SUFFIX (self, d, e, q, error)
	elpa_solve_tridiagonal_d: class method to solve the eigenvalue problem for a tridiagonal matrix a More...

Function/Subroutine Documentation

elpa_choleksy_ELPA_IMPL_SUFFIX_c()

subroutine elpa_choleksy_ELPA_IMPL_SUFFIX_c	(	type(c_ptr), intent(in), value	handle,
		type(c_ptr), intent(in), value	a_p,
		integer(kind=c_int), intent(in), optional	error
	)

elpa_cholesky_ELPA_IMPL_SUFFIX()

subroutine elpa_cholesky_ELPA_IMPL_SUFFIX	(	class(elpa_impl_t)	self,
			a,
		integer, optional	error
	)

elpa_choleksy_d: class method to do a cholesky factorization

The dimensions of the matrix a (locally ditributed and global), the block-cylic-distribution block size, 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).
error	integer, optional: returns an error code, which can be queried with elpa_strerr

elpa_eigenvalues_ELPA_IMPL_SUFFIX()

subroutine elpa_eigenvalues_ELPA_IMPL_SUFFIX	(	class(elpa_impl_t)	self,
			a,
		real(kind=c_real_datatype), dimension(self%na)	ev,
		integer	error
	)

elpa_eigenvalues_d: class method to solve the eigenvalue problem

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).
ev	On output: eigenvalues of a, every processor gets the complete set
error	integer, optional: returns an error code, which can be queried with elpa_strerr

elpa_eigenvalues_ELPA_IMPL_SUFFIX_c()

subroutine elpa_eigenvalues_ELPA_IMPL_SUFFIX_c	(	value	handle,
		value	a_p,
		value	ev_p,
		integer(kind=c_int), intent(in)	error
	)

elpa_eigenvectors_ELPA_IMPL_SUFFIX()

subroutine elpa_eigenvectors_ELPA_IMPL_SUFFIX	(	class(elpa_impl_t)	self,
			a,
		real(kind=c_real_datatype), dimension(self%na)	ev,
			q,
		integer	error
	)

elpa_eigenvectors_d: class method to solve the eigenvalue problem

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).
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.
error	integer, optional: returns an error code, which can be queried with elpa_strerr

elpa_eigenvectors_ELPA_IMPL_SUFFIX_c()

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

elpa_generalized_eigenvalues_ELPA_IMPL_SUFFIX()

subroutine elpa_generalized_eigenvalues_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_d: class method to solve the eigenvalue problem

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_ELPA_IMPL_SUFFIX_c()

subroutine elpa_generalized_eigenvalues_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_ELPA_IMPL_SUFFIX()

subroutine elpa_generalized_eigenvectors_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_d: class method to solve the eigenvalue problem

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_ELPA_IMPL_SUFFIX_c()

subroutine elpa_generalized_eigenvectors_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_hermitian_multiply_ELPA_IMPL_SUFFIX()

subroutine elpa_hermitian_multiply_ELPA_IMPL_SUFFIX	(	class(elpa_impl_t)	self,
		character*1	uplo_a,
		character*1	uplo_c,
		integer(kind=c_int), intent(in)	ncb,
			a,
			b,
		integer(kind=c_int), intent(in)	nrows_b,
		integer(kind=c_int), intent(in)	ncols_b,
			c,
		integer(kind=c_int), intent(in)	nrows_c,
		integer(kind=c_int), intent(in)	ncols_c,
		integer, optional	error
	)

elpa_hermitian_multiply_d: class method to perform C : = A**T * B where A is a square matrix (selfna,selfna) which is optionally upper or lower triangular B is a (selfna,ncb) matrix C is a (selfna,ncb) matrix where optionally only the upper or lower triangle may be computed

the MPI commicators and the block-cyclic distribution block size are already known to the type. Thus the class method "setup" must be called BEFORE this method is used

Parameters

self	class(elpa_t), the ELPA object
uplo_a	'U' if A is upper triangular 'L' if A is lower triangular anything else if A is a full matrix Please note: This pertains to the original A (as set in the calling program) whereas the transpose of A is used for calculations If uplo_a is 'U' or 'L', the other triangle is not used at all, i.e. it may contain arbitrary numbers
uplo_c	'U' if only the upper diagonal part of C is needed 'L' if only the upper diagonal part of C is needed anything else if the full matrix C is needed Please note: Even when uplo_c is 'U' or 'L', the other triangle may be written to a certain extent, i.e. one shouldn't rely on the content there!
ncb	Number of columns of global matrices B and C
a	matrix a
local_nrows	number of rows of local (sub) matrix a, set with class method set("local_nrows",value)
local_ncols	number of columns of local (sub) matrix a, set with class method set("local_ncols",value)
b	matrix b
nrows_b	number of rows of local (sub) matrix b
ncols_b	number of columns of local (sub) matrix b
c	matrix c
nrows_c	number of rows of local (sub) matrix c
ncols_c	number of columns of local (sub) matrix c
error	optional argument, error code which can be queried with elpa_strerr

elpa_hermitian_multiply_ELPA_IMPL_SUFFIX_c()

subroutine elpa_hermitian_multiply_ELPA_IMPL_SUFFIX_c	(	type(c_ptr), intent(in), value	handle,
		character(1,c_char), value	uplo_a,
		character(1,c_char), value	uplo_c,
		integer(kind=c_int), value	ncb,
		type(c_ptr), intent(in), value	a_p,
			b,
		integer(kind=c_int), value	nrows_b,
		integer(kind=c_int), value	ncols_b,
			c,
		integer(kind=c_int), value	nrows_c,
		integer(kind=c_int), value	ncols_c,
		integer(kind=c_int), intent(in), optional	error
	)

elpa_invert_trm_ELPA_IMPL_SUFFIX()

subroutine elpa_invert_trm_ELPA_IMPL_SUFFIX	(	class(elpa_impl_t)	self,
			a,
		integer, optional	error
	)

elpa_invert_trm_d: class method to invert a triangular

The dimensions of the matrix a (locally ditributed and global), the block-cylic-distribution block size, 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).
error	integer, optional: returns an error code, which can be queried with elpa_strerr

elpa_invert_trm_ELPA_IMPL_SUFFIX_c()

subroutine elpa_invert_trm_ELPA_IMPL_SUFFIX_c	(	type(c_ptr), intent(in), value	handle,
		type(c_ptr), intent(in), value	a_p,
		integer(kind=c_int), intent(in), optional	error
	)

elpa_solve_tridiagonal_ELPA_IMPL_SUFFIX()

subroutine elpa_solve_tridiagonal_ELPA_IMPL_SUFFIX	(	class(elpa_impl_t)	self,
		real(kind=c_real_datatype), dimension(self%na)	d,
		real(kind=c_real_datatype), dimension(self%na)	e,
		real(kind=c_real_datatype), dimension(self%local_nrows,self%local_ncols)	q,
		integer, optional	error
	)

elpa_solve_tridiagonal_d: class method to solve the eigenvalue problem for a tridiagonal matrix a

The dimensions of the matrix a (locally ditributed and global), the block-cylic-distribution block size, 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

d	array d on input diagonal elements of tridiagonal matrix, on output the eigenvalues in ascending order
e	array e on input subdiagonal elements of matrix, on exit destroyed
q	matrix on exit : contains the eigenvectors
error	integer, optional: returns an error code, which can be queried with elpa_strerr