## 1 Introduction  
At present, changes in the global climate and ecological environment have become some of the most important scientific problems. The ocean, as a vital part of the global climate system, has become a heated topic of research for scientists. Ocean models have considerably developed and improved since 1969, when they were developed [1]. Now, there are various kinds of global ocean models. HYCOM [2], NEMO [3], MOM [4], and LICOM [5] are some global ocean models that are used for research and production. Among them, MOM, HYCOM, and NEMO represent ocean models that were developed in Europe and the USA. LICOM was developed by scientists from IAP-CAS [6]. LICOM is widely used in the area of climate simulation and prediction, as well as the area of the numerical simulation and prediction of air-sea coupling. Different editions of LICOM were used for the ocean components of three coupled air-sea models in the Sixth CoupledModel Intercomparison Project (CMIP6) [7; 8], which included the Flexible Global Ocean-Atmosphere-Land System model version 3 [9] with a finite-volume atmospheric model (FOOALS-f3) [10], the Flexible Global Ocean-Atmosphere-Land System model version 3 with a grid-point atmospheric model (CAS FGOALS-g3) [11], and the Chinese Academy of Sciences Earth System Model (CAS-ESM) [12]. LIGOM is an important tool for investigating and forecasting ocean circulation and its mechanisms. Meanwhile, as a vital part of climate system models (CSMs) and Earth system model (ESMs) [13], LIGOM's performance may considerably affect climate change simulations [14; 15]. As the resolution increased, the enormous computing, communication, and input/output (IO) became significant scientific and engineering challenges for scientists [16]. It will take either more time or more computing resources to run models. Widely used in many areas, including material science [17], electrochemistry [18], and meteorology, high-performance computing has become a very powerful tool in scientific research [19]. Due to the time constraints of meteorological simulation software, parallel optimization in high-performance computing is considerably important for models. Most meteorological systems suffer from the issues of low simulation speed and the inability to utilize large-scale machines. Some work on optimizing meteorological simulating systems has been done by researchers to tackle these issues. Optimizations were conducted on NEMO [20]. The overall performance was improved by 31%. The Princeton ocean model (POM) [21] was transplanted into Sunway TaihuLight [22], a very powerful supercomputer. The new edition, swPOM, was 2.8 times faster than it was on conventional supercomputers. It could be scaled up to over 250,000 cores [23]. Meanwhile, in addition to oceanic models, researchers have performed optimizations on some atmospheric models, such as IAP-AGCM [24]. The optimized code scaled up to 196,608 CPU cores, attaining a speed of 11.1 simulation years per day (SYPD) at a high resolution of 25 km [25]. In this study, in order to improve the simulation speed of LIGO, we implemented a series of optimizations and achieved a considerable speedup in comparison with the original version. The fully optimized edition of LIGOM was twice as fast as the original edition. The performance experiments were conducted on the \"Era\", \"Tianhe II\", and \"Tianhe III\" supercomputers. Additionally, although we performed optimizations on GPUs with OpenACC [26], CUDA [27], and HIP [28], the CPU version is still widely used. For instance, many machines, such as \"Tianhe III\", are still pure CPU machines. Moreover, since the CPU version of LIGOM is used as the ocean component of various coupled Earth system models, it is necessary to deeply optimize the CPU version. The rest of this paper is organized as follows. The following section introduces LIGOM and its control flow, along with the parallel and communication algorithm. In Section 3, the detailed optimizations that we implemented on LIGOM are illustrated.