Parallel computing applied to breeding value estimation in dairy cattle

Continuous genetic evaluation of dairy cattle with test-day models is desir ed in Finland. However, the computing time for the genetic evaluation is 4 d and exceeds the minimum of a weekend. Three parallel implementations of t he preconditioned conjugate gradient iterative solver were programmed and c ompared to identify the best strategy for solving mixed model equations usi ng parallel computing. The programs were used to solve two random regressio n test-day models with approximately 7.28 and 49.9 million unknowns. The la tter model will be used in the Finnish dairy cattle evaluation. Computing t imes for the smaller model with the four processors available were 52, 32, and 27% of the single processor program when the complexity of the parallel program was increased. In practice, the best program required the most pro gramming because the other parallel programs could not solve the larger mod el because of excess memory requirements. Parallel computing with four proc essors reduced the time to obtain solutions of Finnish dairy cattle evaluat ions to under 2 d. Benefit from parallel computing will be increased if amo unt of computing memory is increased.