Abstract: The efficient implementation of Bayesian and Metropolis Hastings algorithms makes Mrbayes a widely used tool for molecular sequence phylogenetic analysis.However,the increase of molecular sequences and evolutionary parameters leads to the rapid expansion of the sample space of candidate molecular trees,which makes the reconstruction of phylogenetic trees face great computational challenges.In order to reduce the calculation time of conditional likelihood probability of molecular tree in mrbayes phylogenetic analysis and improve the analysis efficiency,a number of parallel acceleration methods based on graphics processor(GPU) have emerged in recent years.In order to improve the scalability of parallel methods,an optimized likelihood probability multithreaded parallel computing method is proposed in this paper.As the calculation of molecular state likelihood probability in the variable evolution rate model between sites needs to correspond to different transition probability matrices,this method further decomposes the parallel calculation of likelihood probability of different sites using multithreading into the calculation of conditional likelihood probability under different transition probability matrices between multiple sites.This strategy optimizes the parallel overlap between threads and improves the parallel efficiency by increasing the number of threads without changing the calculation transmission ratio of a single thread.In addition,because each thread warp only calculates the likelihood probability under the same transition probability matrix,it avoids the synchronization overhead between different warps when using shared memory,and further improves the computing efficiency of the kernel.Calculation results of 4 groups of actual data and 30 groups of simulated data show that the computational performance of this method is 1.78 and 2.04 times higher than that of tgMC³(version 2.0) and nMC³(version 2.1.1) in the calculation acceleration of core likelihood function.

Key words: MrBayes, Likelihood computing, GPU, Parallel computing, CUDA