Perumalla Kalyan S, Alam Maksudul
Oak Ridge National Laboratory, Oak Ridge, TN USA.
J Indian Inst Sci. 2021;101(3):357-370. doi: 10.1007/s41745-021-00253-1. Epub 2021 Aug 3.
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与分析中，一个主要挑战在于解决模型逼真度与其模拟速度之间的冲突。在处理大量需要探索的假设情景时，还会出现另一个相关挑战。在此，我们描述了一些新的计算方法，它们共同提供了一种应对这两个挑战的途径。我们描述了一种介观建模方法，它在基于耦合微分方程的宏观模型与基于个体实体层面细粒度行为构建的微观模型之间找到了一个中间地带。介观方法能够纳入多层动力学的复杂组成，同时还保留了不同层面上总体行为的可能性。它也与现代计算平台基于加速器的架构完美匹配，在这种架构中，可以通过单指令多线程（SIMT）的并行执行模式利用图形处理单元（GPU）进行快速模拟。通过一种在假设情景树中共享模型状态和计算的方法来应对模拟大量情景的挑战，这些假设情景是对大型基础模拟的局部、增量变化。介观建模方法与增量假设情景树评估的结合已在现代GPU上的软件中实现。我们展示了合成模拟情景以说明我们方法的计算特性。来自包括美国、英国和印度在内的大量人口数据的实验结果，说明了在数千个合成生成的假设情景上的建模方法和计算性能。在超级计算平台的8192个GPU上执行我们的实现，证明了能够比传统方法快几个数量级地快速评估假设情景。
