## 1 Introduction  
The Korea Institute of Science and Technology Information (KISTI) recently installed the 5 th supercomputer Nurion system, which exhibits theoretical and actual performances of 25.7 PF and 13.92 PF, respectively. Nurion has 8305 computing nodes (approximately 570,000 cores) and is 70 times faster than the performance of the 4 th supercomputer which has approximately 30,000 cores and 3400 nodes. Nurion had the 15 th highest performance rate (based on actual performance) out of the world's TOP500 Supercomputers in November 2019 ([[https://www.top500.org/list/2019/06](https://www.top500.org/list/2019/06)]([https://www.top500.org/list/2019/06](https://www.top500.org/list/2019/06))). The Nurion system is installed on an SSD layer to speed up data movement between its CPU, memory, and disk. Its storage capacity is approximately 21 PB. Nurion has two types of CPUs: an Intel Xeon Phi 7250 Knights Landing (KNL, 8305 nodes) processor and an Intel Xeon 6148 Skylake (SKL, 132 nodes) processor.  
The KNL processor was released to be fully compatible with Intel Xeon and run the operating system independently on manycore first generation products [1].  
The Nurion KNL system has 68 cores per CPU and 96 GB of allocated memory, while the Nurion SKL system has 20 cores per CPU and 192 GB of allocated memory, thereby indicating that the KNL system has relatively low memory. In addition, the speed of the KNL is at 1.4 GHz frequency, which is slower than the speed (2.5-3 GHz) of the SKL system. Therefore, KNL's performance may be less than 50% of SKL system.  
The white paper [2] from DELL EMS in June 2018 compared the performances of the SKL and KNL systems with several numerical models. The Weather Research and Forecasting (WRF) model was also used to compare the performances of both systems in the paper. An experiment for the continental United States (CONUS) was conducted using WRF v3.6.1 as a benchmark model with a 2.5 km resolution, and it was applied to the inputs and outputs by the parallel-netcdf 1.8.1 library for parallel optimization. This configuration can improve the performance of a KNL system with many cores and low memory in distributing reading and writing model results. Although the white paper was intended to promote the superiority of the SKL system, the performance of the KNL system was nonetheless two to three times slower than the SKL system.  
[PERSON] et al. (2017) found that the performance of parallel MPI with the same nodes was improved by up to 272% when using on-package memory (Multi-Channel DRAM, MCDRAM), which is one of the features of the KNL system [3]. [PERSON] et al. (2018) studied optimization methods for data types that are not suitable for MCDRAM on a KNL system [4]. [PERSON] et al. (2017) discussed a solution for performance bottlenecks that can occur when running MPI applications on a KNL system [5], and [PERSON] and [PERSON] (2019) analyzed the features of manycore architectures such as the KNL system [6]. However, these studies were not conducted using general numerical models used in real circumstances and did not include the effects of inputs and outputs.  
In this study, the performance of the Nurion KNL system was compared with general cluster systems using three numerical models: the Weather Research and Forecasting System (WRF), Regional Ocean Modeling System (ROMS) and Unstructured Grid Finite Volume Community Ocean Model (FVCOM). Ideal and real experiments were simulated using the characteristics of each numerical model, and the performance rates were compared via wall-clock simulation time. All experiments were also performed on a general cluster system (a large-scale cluster system at Jeju University, referred to as CLS) to objectively compare its performance with that of the Nurion system.