In order to improve the efficiency of soybean genetic variation information, robust relationship inference in GWAS parallel algorithm is raised in this paper. This algorithm is adopted serial and parallel hybrid structure. Each row of data is parallel processed by OpenMP technique. The I/O processing and the accumulate processing are treated as a serial. Finally, the serial and parallel algorithms are compared and analyzed. The results reached theoretically exceptional speedup. The experimental results show that the execution efficiency of parallel algorithm has very big promotion than the serial algorithm. The soybean genetic variation information processing can be speed up effectively.

The experimental data is soybean gene variation information file. The individual biological information is extracted from the soybean gene file and the value of the relationship between populations is computed. Then the degree of differentiation of the two populations of wild soybean and cultivated soybean is obtained. Firstly, the easy parallel serial algorithm is designed. Then according to the independence of the operational object, the partial algorithm can be paralleled is parallel processed with GPU multi thread technology. Finally, the result and the time of the serial / parallel algorithm are analyzed and compared. The experimental results show that the GPU algorithm efficiency is much higher than the serial algorithm efficiency. It provides a quick calculation scheme to handle massive biological data.

The key algorithm of parallel technology based on OpenMP is advanced to dispose the promoter data. The promoter data are intercepted from the upstream area of five plants sequences. The five plants contain Arabidopsis Thaliana, Soybean, Alfalfa, Rice and Beans. The processing includes disturbing the sequences in random, calculating the P value, selecting the significant motifs from the frequent motifs, labeling the sequence number and so on. The serial algorithm and the parallel algorithm are compared in the implementation procedure. The experimental result shows that the OpenMP parallel technology can effectively improve the efficiency of the promoter data analysis and processing. It is a realistic significance to the vast amounts of biological information data processing.