HLIBpro
2.0

Table of contents 
In this example, an integral equation is to be solved by representing the discretised operator with an πmatrix, whereby the πmatrix is constructed using a matrix coefficient function for entries of the equation system. Furthermore, for the iterative solver a preconditioner is computed using πLU factorisation.
Given the integral equation
with being sought for a given right hand side , the Galerkin discretisation with constant ansatz functions
leads to a linear equation system where contains the coefficients of the discretised and is defined by
The right hand side is given by
The code starts with the standard initialisation:
Together with the initialisation, the verbosity level of HLIBpro was increased to have additional output during execution. The corresponding function set_verbosity
is available in the namespace HLIB::CFG
.
For clustering the unknowns, coordinate information is to be defined. For this 1d example, the indices are placed in the middle of equally sized intervals.
Coordinates in HLIBpro are vectors of the corresponding dimension, e.g. one in for this problem. The set of all coordinates is stored in objects of type TCoordinate.
The usage of smart pointers, e.g. unique_ptr
, is advised, and extensively used in HLIBpro, since it decreases the possibility of memory leaks by automatically deleting the objects upon destruction of the spart pointer variable, e.g. when leaving the local block.
Having coordinates for each index, the cluster tree and block cluster tree can be constructed.
For the cluster tree, the partitioning strategy for the indices is determined automatically by TAutoBSPPartStrat. Furthermore, a of size 20 is used during construction.
For the block cluster tree, the standard admissibility condition (see {eqn:std_adm}) implemented in TStdAdmCond is used with .
Using the block cluster tree, finally the πmatrix can be constructed. For this, adaptive cross approximation (ACA, see ???) is applied to compute low rank approximations of admissibly matrix blocks. In HLIBpro, an improved version of ACA is implemented in TACAPlus.
ACA only needs access to the matrix coefficients of the dense matrix. Those are provided by TLogCoeffFn, which will be discussed below. Finally, the block wise accuracy of the πmatrix approximation has to be defined using TTruncAcc objects. In this case, an accuracy of is used.
CFG::set_nthreads()
.The coefficient function used during matrix construction is implemented in class TLogCoeffFn.
It is derived from TPermCoeffFn, where the interface is defined for coefficient evaluation with permuted indices. The reordering is necessary, since due to the construction of the cluster tree the numbering of the indices in the πmatrix is usually different from the corresponding numbering in the application. The latter is referred to as the external numbering, whereas the first is called the internal numbering.
Going on with TLogCoeffFn, the step width for the grid is stored as an internal variable for kernel evaluation:
In the constructor, the permutation for mapping the internal to the external numbering are provided, both for rows and for columns, together with the step width:
The actual interface for the evaluation of the matrix coefficients is implemented by the function eval
, which is defined in TPermCoeffFn and has to be overloaded:
This functions gets a set of indices for rows (rowidxs
) and columns (colidxs
) and a pointer to a memory block, where all the coefficients should be stored. The indices in both sets are already in the external numbering, hence the indices can directly be used for kernel evaluation. The mapping was performed by TPermCoeffFn. The memory layout of matrix
is column wise, which holds for almost all data in HLIBpro. This is due to the memory layout of BLAS/LAPACK, originally implemented in Fortran, which uses column wise storage.
TPermCoeffFn is derived from TCoeffFn, which uses a slightly different evaluation interface based on index sets in the internal numbering. TPermCoeffFn overloads this method with the conversion between both numberings and implements the new eval
method. To avoid compiler warnings about ``hidden functions'', the first version is brought into local scope by
By default, the format of the final matrix defined by the coefficients will be unsymmetric, e.g. lower and upper half of the matrix will be built. Please note, that this will not effect the actual algebraic matrix which may still be symmetric or hermtitian. Only the storage and subsequent algorithms are affected in terms of computational costs.
To change the matrix format, the function matrix_format
has to be overloaded. In this case, as the matrix is symmetric, this looks like
Finally, TLogCoeffFn has to signal the value type, e.g. real or complex valued, to the matrix construction object with the method is_complex
.
It should be noted, that for a complex coefficient function, to evaluation method is not called eval
but ceval
.
Having constructed the πmatrix, the corresponding equation system shall be solved. In most cases, a standard iterative solver will not be sufficient, e.g. either the convergence rate is to bad or there is no convergence at all. Therefore, a (good) preconditioner is needed, with the inverse of being the best possible. Since iterative schemes only need matrixvector multiplication, the corresponding functionality for the inverse is sufficient. This is provided by an (π) LU factorisation of , or for the symmetric case, a LDL factorisation.
For this, either the factorisation classes may be used directly or, instead, the function factorise_inv
be empoyed, which chooses uses a LU factorisation for unsymmetric matrices and a LDL factorisation otherwise. The return value is an object providing the functionality of a linear operator, e.g. evaluation of vectors. In this case, it corresponds to an operator for the evaluation of the inverse.
The copy of A
is neccessary, since the matrix is modified during the factorisation. The accuracy is chosen to be the same as during matrix construction.
Still missing is the right hand side of the equation system. In this example, it was chosen, such that :
with rhs
defined by
For the iterative solver TAutoSolver is used, which automatically chooses a suitable solver for the given combination of matrix and preconditioner. Furthermore, statistics about the solution process is stored in an object of type TSolver::TInfo, e.g. convergence rate, number of steps.
To check the accuracy of the computed solution, we compare it with the known exact solution:
The standard finalisation and catch
block finishes the example: