Computes the LU factorization of a general complex sparse matrix by a left-looking column method using OpenMP parallelism and solves the complex sparse linear system of equations Ax = b.

The type d_complex functions are imsl_z_superlu_smp and imsl_z_superlu_smp_factor_free.

A pointer to the solution x of the sparse linear system Ax = b. To release this space, use imsl_free. If no solution was computed, then NULL is returned.

The steps imsl_c_superlu_smp uses to solve linear systems are identical to the steps described in the documentation of the serial version imsl_c_superlu.

Function imsl_c_superlu_smp uses a supernodal storage scheme for the LU factorization of matrix A. In contrast to the sequential version, the consecutive columns and supernodes of the L and U factors might not be stored contiguously in memory. Thus, in addition to the pointers to the beginning of each column or supernode, also pointers to the end of each column or supernode are needed. The factorization is contained in structure Imsl_c_super_lu_smp_factor and its two sub-structures Imsl_c_hbp_format and Imsl_c_scp_format. Following is a short description of these structures:

Structure Imsl_z_super_lu_smp_factor and its two sub-structures are defined similarly by replacing float with double, f_complex with d_complex, Imsl_c_hbp_format with Imsl_z_hbp_format, and Imsl_c_scp_format with Imsl_z_scp_format in their respective definitions.

In contrast to the sequential version, the numerical factorization phase of the LU decomposition is parallelized. Since a dynamic memory expansion as in the serial case is difficult to implement for the parallel code, the estimated sizes of array rowind for the L and of arrays rowind and nzval for the U factor (see structures Imsl_c_scp_format and Imsl_c_hbp_format above) must be predetermined by the user via elements 6 and 7 of the performance tuning array sp_ienv.

In order to ensure that the columns of each L supernode are stored contiguously in memory, a static or dynamic prediction scheme for the size of the L supernodes can be used. The static version, which function imsl_c_superlu_smp uses by default, exploits the observation that for any row permutation P in PA = LU, the nonzero structure of L is contained in that of the Householder matrix H from the Householder sparse QR factorization A = QR. Furthermore, it can be shown that each fundamental supernode in L is always contained in a fundamental supernode of H. Therefore, the storage requirement for the L supernodes and array nzval in the L factor respectively can be estimated and allocated prior to the factorization based on the size of the H supernodes. The algorithm used to compute the supernode partition and the size of the supernodes in H is almost linear in the number of nonzeros of matrix A.

In practice, the above static prediction scheme is quite tight for most problems. However, if the number of nonzeros in H greatly exceeds the number of nonzeros in L, the user can try a dynamic prediction scheme by setting optional argument IMSL_SNODE_PREDICTION to 1. This scheme still uses the supernode partition in H, but dynamically searches the supernodal graph of L to obtain a much tighter upper bound for the required storage. Use of the dynamic scheme requires the user to define the size of array nzval in the L factor via element 5 of the performance tuning array sp_ienv.

For a complete description of the parallel algorithm, see Demmel et al. (1999c).

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The LU factorization of the sparse complex 6×6 matrix

is computed. Let

so that

and

The LU factorization of A is used to solve the sparse complex linear systems Ax=b, ATx=b1, and AHx=b2.

This example uses the matrix A = E(1000,10) to show how the LU factorization of A can be used to solve a linear system with the same coefficient matrix A but different right-hand side. Maximum absolute errors are printed. After the computations, the space allocated for the LU factorization is freed via function imsl_c_superlu_smp_factor_free.