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Parallel Genomic Prediction Method Based on CPU-GPU

Genomic prediction uses SNP marker data and phenotypic data to estimate locus effect values, which belongs to a highly intensive computing task. The calculation task of gene combination matrix in this study was conducted by GPU and CPU. Load balancing in GPU was used for task allocation to improve the calculation speed of gene combination matrix. Those in matrix computing, which could not be allocated in GPU, were calculated in CPU. The estimation of gene locus effect value was implemented using Expectation Maximization algorithm, and the calculation of expectation maximization was completed using OpenMP technology in a multi-core CPU. The genomic breeding estimation values were calculated with the estimated locus effect values and the SNP maker values in CPU to realize genomic breeding value estimation based on CPU-GPU heterogeneous parallel technology. We used 4 traits of Intermediate Wheatgrass data set to evaluate this method, and results showed that the accuracies were close to those of BayesA, BayesB and Bayescπ, and the running times were lower than those of of BayesA, BayesB and BayesCπ. Comprehensive comparison showed that the method is more practical.