Bayesian inference is one of the most important methods for estimating phylogenetic trees in bioinformatics. Due to the potentially huge computational requirements, several parallel algorithms of Bayesian inference have been implemented to run on CPU-based clusters, multicore CPUs, or small clusters of CPUs and GPUs. These phylogenetic programs utilize a Markov Chain Monte Carlo (MCMC) method for sampling tree and parameter space. We are interested in optimizing the MCMC approach for Bayesian phylogenetic analyses. In an attempt to define the optimal strategy we will test more chains or more runs on Magnus (CPU) and Fornax (GPU). This aims to increase food security initially in Uganda, Tanzania and Malawi, and Australia by reducing the spread of whitefly-borne cassava-virus pandemics. This will be achieved by carrying out research to understand factors that drive populations of the vector of these diseases, African cassava whitefly, to become super-abundant.