Parallel partitioned inverse method for least-squares adjustment

Parallel computing is undoubtedly the trend in numerical applications of hi ghly intensive computation. There has been much related research and develo pment on parallel computer architecture, algorithm design, and supplementar y packages. However, computational technology has seen little interest in t he surveying area since the North American Datum of 1983 adjustment. In thi s research, a parallel partitioned inverse algorithm is implemented and app lied to a least-squares adjustment of horizontal survey networks to present the potential of parallel computing methods for surveying data. Two observ ation data sets with 2,412 and 1,902 unknowns were used for the test. To im prove performance of the algorithm, two different partitioning schemes also were investigated with the data sets. The computational experiment present s the good scalability of the algorithm and batter partitioning approach wi th the improved speed. However, it is noted that parallel factorization of sparse matrices is required to fully utilize the proposed approach.