## 1 Introduction  
Simulation has been widely used to solve various problems in dynamic systems where the characteristics of a system are not statically described in analytic equations. discrete event system specification (DEVS)-based modeling and simulation (M&S) [1] has been widely adopted as an efficient simulation framework by splitting the model description part and simulation-related tasks. An M&S approach for event models, which are described with the state transitions by discrete events, has advantages when it comes to evaluating simulation models in terms of the bandwidth and speed compared with a simulation based on a discrete time-step. DEVS-based M&S has been applied and studied [2; 3; 4] to accelerate the simulation speed with a reasonable fidelity for a hybrid system with complex behaviors, which are simultaneously described in a discrete event model and continuous time model. The hybrid system has continuous and discrete dynamic behavior, and it is characterized by interactions between the continuous system and the discrete event system. For this reason, DEVS-basedhybrid system simulation has been widely used for expressing [3] and simulating complex systems and has been studied in various fields [5; 6]. In most cases, however, these hybrid systems are more complex and require more time than discrete event-based simulations because the continuous system has to be scheduled frequently in the manner of a small discrete time step in a simulation kernel. DEVS-based simulation studies using cellular models have been introduced as an extension of DEVS M&S formalism [7]. The cellular model is defined as a grid of cells, for which the discrete events describe the relation between cells and the internal state transitions are based on continuous time models such as differential equations. DEVS-based cellular models can be applied in hybrid systems with differential-equation-based state transitions and event-driven propagation models such as fire-spreading. Cellular models consist of a large number of cells, which are represented with the detailed model description in continuous time domain, and event-driven model evaluation of cells in hybrid systems have big overhead in communicating with the cross-coupled events. To mitigate the drawback caused by this style of hybrid description of cellular models, aggressive enhancement in a simulator's kernel level has to be considered to accelerate simulation speeds. The parallel simulation studies have been presented [8], parallelizing DEVS-based simulation in multiple cores [9]. In addition, parallelization approaches using graphic processing units (GPUs), which have an advantage in performing large amounts of computation, have been introduced [10]. GPUs are used in many research fields because they make it easy to convert iterative operations for performing tasks in the parallel way. GPU-based simulation enables integrated acceleration for continuous and discrete-event models by executing control flows in sequential algorithms on the small number of CPUs executing and large number of parallelized tasks on the GPU. In this study, we describe the accelerated process of simulation and DEVS-based hybrid systems, which possess discrete system and continuous system behaviors. We use large-scale cellular model simulations, which can be applied not only to a homogeneous system but also to the modeling of an Internet of Things (IoT) system, such as a smart farm and a smart factory. We propose the distribution of the simulation process to the multi-cores and GPUs of a large-scale cellular model following hybrid systems. In addition, we propose parallelizing a simulation using a GPU. We simulate in the proposed heterogeneous architecture, which involves the coupling of multi-cores and GPU-based systems and analyze the result by changing the simulation parameters. The simulation time is also analyzed by composing several configurations of target devices according to the number of devices.