In this paper, we examine load distributions to minimize total run time in multi-cluster parallel computing algorithms by applying divisible load theory techniques.??Even with homogeneous processor speeds, parallel computations in multi-clusters that evenly assign load can run at less than maximum efficiency due to communication heterogeneity.??Using a modified version of the LogP parallel computing model, we propose a general technique of assigning load among multiple clusters to minimize the time each processor spends waiting.??This technique is used to determine optimal load distribution for spin glass simulation and parallel bucket sort in multi-cluster systems.??It also allows fast analysis of the effects of adding processors or clusters to the computation.??We experimentally demonstrate the accuracy of our model, and show how it eliminates wait time in multi-cluster parallel computations.??Using load distributions derived from our technique results in an execution time decrease of up to 50%, depending on the degree of heterogeneity among clusters and communication characteristics of the computation.