## 4 Proposed Architecture  
### Multi-Core and GPU Tightly Coupled Simulation  
To achieve accelerating a large-scale hybrid system simulation in a multi-core and a GPU-coupled architecture, it is essential to divide the simulation process among each device on the characteristics of both systems and devices. GPUs are particularly beneficial when applying the same instruction to multiple data and calculating the amount of floating point data. The large-scale hybrid system models can be applied to the complex-connected IoT applications, which consist of multiple heterogeneous models such as sensors, processors, networking and actuators. The complex behavior of IoT-driven applications can be generally modeled with the hybrid modeling specification to extend DEVS formalism, so the proposed simulation framework is widely adopted to perform modeling and simulation (M&S) for IoT-driven applications. The continuous time models and discrete-event models of the given IoT applications can be efficiently partitioned and allocated on the CPU and GPU to perform the accelerated simulation by considering the connected behavior of hybrid system models. Figure 2: Specification of a hybrid system. The continuous-time models, which are tightly coupled with event-driven fire sensor IoT node models, can be converted into discrete-event simulation models using a small time-advance step. This conversion approach is enabled by transparent translation without any model modification, but it requires tremendous simulation time synchronization and communication data elaboration. Simulation for continuous-time model updates the continuous state value in high resolution time step, such as evaluation of continuous differential equation and synchronizes the time and data with the correlated models, so the simulation kernel intervention frequently happens after model evaluation finished. Compared to simulator interactions of event-driven models, the simulation time synchronization and communication for sharing the update data result in a lot of computation overhead. We adopted a GPU with a powerful floating-point computing unit and efficiently distributed the converted event-driven models into CPUs and GPUs, so that, by evaluating the continuous-time models in GPU, the fire-spreading behavior could be simulated in a DEVS-based event-driven single-simulation framework by triggering the events on the event-driven sensor IoT nodes in a large-scale parallel manner. The entire simulation process is divided into multi-cores for discrete-event simulation and GPUs for continuous-time simulation, as displayed in Figure 3.  
To provide more detail about the proposed architecture for distributing large-scale hybrid systems to multi-cores and GPUs, we use a cellular model as an example. The cellular model used for the target model in this study consists of multiple cell connections, and each cell has an internal state change that follows differential equations that can be modeled by CS. The large-scale cell model can consist of a large number of cells, such as K cells. In this case, each cell has a CS model based on a discrete time system. Therefore, the process of simulating the CS of each cell that follows the differential equations is repetitive, and if these repeated models are simulated in GPUs, the result will be effective in terms of the simulation speed. Following is an important consideration proposed in this study for implementing the DEVS-based multi-core and GPU framework. To perform tasks in the GPU, the host CPU is required to copy calculation data into shared memory in the GPU, which is one of the major causes that limits the performance enhancement in CPU-GPU heterogeneous architecture, although the GPU has the advantage of accelerating large amounts of repetitive computation.