Task granularity is a key factor in determining the performance of asynchronous many-task (AMT) runtime systems. The over-head of scheduling an excessive number of tasks with smaller granularities causes performance degradation while creating a few larger tasks leads to starvation and therefore under-utilization of resources. In this paper, we developed an analytical model of the execution time of an application with balanced parallel for-loops in terms of grain size, and number of cores. The parameters of this model mostly depend on the runtime and the architecture. We introduce an approach to suggest a range of possible grain sizes to achieve the best performance based on the proposed model. To the best of our knowledge, our analytical model is the first to explain the relationship between the execution time in terms of grain size, runtime, and physical characteristics of the machine in an asynchronous runtime system.