Optimizing Discrete Simulations of the Spread of HIV-1 to Handle Billions of Cells on a Workstation

Cellular Automata have been used on many occasions to model the spread of the Human Immunodeficiency Virus (HIV) within a human body. This is in part due to the relative simplicity of crafting their rules and the convenience of visualizing disease dynamics in 2D. Although such models appeared in 2001 and have been extended in dozens of studies, their potential to serve as a virtual laboratory has been limited by their computationally intensive nature. So far, they have been used to simulate at most 0.5 million cells instead of the billion cells that may harbor the virus. Simulating too few cells is a key issue for calibration (the 'small' models are calibrated based on results observed in a much larger space), prevents us from using a sufficient proportion of cells to model latent HIV reservoirs (in which HIV can hide for years), and prohibits even more computationally intensive aspects such as tracking mutations (which is essential to assess drug resistance). In short, the low number of cells prevents these models from answering many of the questions that would make them useful as virtual laboratories. Although the models may be scaled by running on clusters, this is not always an option since interdisciplinary research in discrete models of HIV often takes place on the lab's computer, and patients for whom we seek to provide virtual laboratories may have limited access to computational resources. Given these constraints, we demonstrate how to optimize simulations of HIV on a workstation by combining features such as just-in-time compilation, parallelism at the level of threads, pseudo random number generators, and simplified handling of neighbors in a cellular automaton. Our results demonstrate that, within 10 minutes, we can finish a simulation run for 6.7 billion cells instead of 60,000 cells in an unoptimized simulation.