In simulation-based studies and analyses of epidemics, a major challenge lies in resolving the conflict between fidelity of models and the speed of their simulation. Another related challenge arises in dealing with the large number of what–if scenarios that need to be explored. Here, we describe new computational methods that together provide an approach to dealing with both challenges. A mesoscopic modeling approach is described that strikes a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors at the individual entity level. The mesoscopic approach offers the ability to incorporate complex compositions of multiple layers of dynamics even while retaining the potential for aggregate behaviors at varying levels. It also is an excellent match to the accelerator-based architectures of modern computing platforms in which graphical processing units (GPUs) can be exploited for fast simulation via the parallel execution mode of single instruction multiple thread (SIMT). The challenge of simulating a large number of scenarios is addressed via a method of sharing model state and computation across a tree of what–if scenarios that are localized, incremental changes to a large base simulation. A combination of the mesoscopic modeling approach and the incremental what–if scenario tree evaluation has been implemented in the software on modern GPUs. Synthetic simulation scenarios are presented to demonstrate the computational characteristics of our approach. Results from the experiments with large population data, including USA, UK, and India, illustrate the modeling methodology and computational performance on thousands of synthetically generated what–if scenarios. Execution of our implementation scaled to 8192 GPUs of supercomputing platforms demonstrates the ability to rapidly evaluate what–if scenarios several orders of magnitude faster than the conventional methods.

在基于模拟的流行病研究和分析中，一个主要挑战在于解决模型保真度与模拟速度之间的冲突。另一个相关的挑战是在处理大量需要探索的假设场景时出现的。在这里，我们描述了新的计算方法，这些方法共同提供了应对这两个挑战的方法。描述了一种介观建模方法，该方法在基于耦合微分方程的宏观模型和基于个体实体级别的细粒度行为构建的微观模型之间取得了中间立场。介观方法提供了整合多层动力学的复杂组合的能力，甚至同时保留了不同级别的聚合行为的潜力。它还与现代计算平台基于加速器的架构完美匹配，在现代计算平台中，可以利用图形处理单元 (GPU) 通过单指令多线程 (SIMT) 的并行执行模式进行快速仿真。模拟大量场景的挑战是通过一种在假设场景树上共享模型状态和计算的方法来解决的，这些假设场景是对大型基础模拟的局部增量更改。现代 GPU 上的软件已实现了介观建模方法和增量假设场景树评估的结合。提出了综合模拟场景来演示我们方法的计算特征。包括美国、英国和印度在内的大量人口数据的实验结果说明了数千个综合生成的假设场景的建模方法和计算性能。 我们的实现扩展到超级计算平台的 8192 个 GPU，这表明我们能够快速评估假设场景，速度比传统方法快几个数量级。