## 2 Related Works  
The simulation of complex systems using a hybrid system based on DEVS has been used and studied in various fields [5; 6; 11]. In addition, many studies have been done on parallelism studied [12; 13; 14], also using partitioning models to sub-models [9]. Various approaches in parallel simulation for DEVS-based models have been proposed to resolve the synchronization issues during simultaneous evaluation of multiple models. A parallel simulation method using null-messages and lookahead-based synchronization mechanism has been introduced [15]. A method to reduce the inter-process communication overhead in parallel simulations has been introduced by flattening the hierarchical model structure [16]. In parallelizing the simulation of DEVS-based hybrid system models, the simulation time could be reduced by delaying the synchronization between continuous models and discrete-event models within allowable time [17]. These previous approaches are based on enhancing the simulator architecture on general CPU architecture. Our approach starts from the detailed understanding of the given hybrid system models. By exploiting the CPU-GPU and dedicated memory, the continuous models and discrete-event models are efficiently partitioned into CPU-GPU architecture and the runtime simulation data is allocated on the GPU memory to reduce the copy overhead from simulation kernel on CPU. Some previously conducted studies used another architecture as a co-processor to parallelize DEVS. Studies have been carried out to speed up the DEVS-based simulation process using GPUs as co-processors [10; 18]. As part of DEVS simulation, the cellular model has been studied based on DEVS with its homogeneous features [19; 20], especially spreading phenomena such as fire-spreading [21; 22]. In cellular model simulation, generally, the greater the number of cells, the more detailed simulation results can be obtained, but more computation is required. To solve this problem, a large-scale cellular model was studied in terms of high-performance [23]. The discrete-event model simulation acceleration using GPU has been introduced [18]. This study showed a GPU utilization method to mitigate the inefficient simulation evaluation caused by the irregular behavior in the time advance of simulation. For parallel simulation of cellular models, the simulation evaluation mainly executed on CPU and GPU is used as an accelerator [10]. In contrast to the existing GPU-based DEVS parallelization studies, our approach is to accelerate the operation of a DEVS-based hybrid system and to verify the high-performance of the proposed architecture by using large-scale cellular models, which can generate detailed simulation results. In addition, we propose simulation acceleration and enhancement methods by mitigating the bottleneck caused by parallel access to GPU memory, which is driven by multiple CPUs executing the parallel simulations. Our proposed study focuses on exploring efficient utilization of CPU-GPU and collaboration of heterogeneous models in extending hybrid simulation using DEVS-driven simulator. We found that memory access pattern during simulation is caused by the structure of hybrid models, which is one of the major causes of slow simulation, even though powerful GPU has been adopted. Simulation of continuous models on GPU requires additional time cost in the data copy from system memory, and multiple CPUs simultaneously access GPU causing a bottleneck effect in accessing GPU memory. Our approach tries to partition structure of hybrid models into simulation models on multiple CPU and efficiently allocate runtime simulation data on GPU memory and try to reduce the bottleneck of simultaneous accesses to GPU, which is powered by an multi process service (MPS) feature of compute unified device architecture (CUDA). In this paper, we aim to implement a tightly coupled simulation architecture for DEVS-based event-driven IoT applications.