## 2 Experimental Configuration  
The specifications of Nurion and general cluster systems are summarized in Table 1. In this study, Intel Fortran v17.x and v19.x were used, which are the basic Fortran compilers installed on the Nurion system. OpenMPI v3.x and IMPI v19.x were used for the MPI library and NetCDF v4.x was used for accessing the inputs and outputs of the models' data. However, IMPI v18.x was used in the CLS system; despite the different compiler versions being used in the CLS and Nurion systems, there was no difference in performance between versions in this study. Ideal and real experiments were configured by considering the characteristics of each model. The models were built with an ultra-high resolution for running parallel experiments using hundreds of cores. Initial Conditions (I.C.) and the Boundary Conditions (B.C.) were also created for the ultra-high resolution models. The compiler option of \"-xMIC-AVX512\" was used to optimize the Many Integrated Cores (MIC) of the KNL system, while \"-xCORE-AVX512\" was used for the SKL system (Table 1, Compiler Options). For model performance analyses using hundreds of cores, the model grid resolution was required to be at least 100 (latitude) \(\times\) 100 (longitude) = 10,000 grids per core. If the number of grids per core was too small, then the performance depended largely on the network performance between CPUs and nodes rather than the CPU speed. For example, an ultra-high resolution model should have a configuration of at least \(1000\times 1000\) for 100 CPU cores. In this study, ideal and real experiments were constructed for each model as shown in Table 2. Real cases are more difficult to simulate than ideal casesbecause topography and external forces must be considered in real situations. Additionally, accounting for the massive amounts of I/O data accessing and preventing model overflows are also necessary. The total grid numbers were configured for the different models by reflecting the characteristics of each experiment. The Nurion KNL/SKL systems have one/two CPUs per node, respectively, and 68/40 cores per node, respectively. The CLS system has two CPUs per node and 36 cores per node. Therefore, 32 cores per node were used for the SKL and CLS systems while 64 cores per node were used for the KNL system for the objective performance evaluation. However, real experiments using all cores per node for ROMS and FVCOM could not be simulated on the KNL system because of its low memory and the requirement that systems must be able to read and write topography, initial fields, external forces, and large outputs for real experiments. Although applying and modifying the parallel I/O method of the models can account for the lack of memory in the KNL system, this solution is out of the scope of this study. To solve this problem, a simulation was attempted by reducing the number of cores per node by half and increasing the number of nodes (refer to Table 3). Additional experiments using the WRF were performed to analyze the performance according to the number of cores per node in the KNL system. The Nurion system is used concurrently by several users; therefore, the average performance speed was used for the objectivity analysis by simulating each experiment more than twice.