# Static Evaluation of a Midimew Connected Torus Network for Next Generation Supercomputers
## 1 Introduction  
One of the challenging issues in implementing supercomputers is the topology involved in connecting a massive number of nodes. It is necessary for the enormous computational power of these supercomputers to progress towards a more sustainable world, which will provide better healthcare, better use of natural resources, sustainable agriculture, and energy-efficient machinery and industry. To achieve these goals, it is required to advance complex modeling, comprehensive simulations, and big data analysis, which are all computation-intensive tasks. Massively parallel computers (MPCs) have been broadly utilized to perform these tasks. The role of MPCs in building sustainable applications is of great interest, as they could lead to a next-generation agricultural revolution with the assistance of artificial intelligence (AI). MPCs utilize resources to provide important functionalities, such as image-based plant disease detection, plague control, pesticide design, understanding pesticides effects, and food supply-chain traceability [1, 2]. Massive amounts of seed data have been analyzed to identify plants with the Rubisco enzyme, which aids in the conversion of plant carbon into biomass [3]. Agricultural water management (AWM) was able to integrate multidisciplinary models that used MPCs [4]. Workflows for agricultural and livestock-farming applicationshave been designed through hybrid data analytics [5]. Enormous computational power is required to implement deep-learning networks for the price forecasting of agricultural products, which has a significant impact on the profitability of agricultural products [6]. AI has been applied in several approaches to climate pattern prediction and weather forecasting [7; 8] to increase agricultural productivity and sustainability [9]. The use of models for assessing future risks that are related to climate change and its impact on agriculture is critical [10]. The genome sequencing of bacterial species to enhance plant growth in order to overcome climate challenges and limited resources has been conducted using supercomputers [11]. The role of MPCs is crucial in providing sustainable healthcare services. In recent years, they have advanced in order to handle the unusually large and distributed medical and non-medical data related to COVID-19 [12] by enabling the tracking of infected people and effectively monitoring whole communities [13; 14]. Supercomputers were used to analyze the genome sequence of the virus in order to identify an appropriate vaccine [15; 16; 17]. Another important domain of supercomputers in healthcare is telemedicine, which includes the diagnosis, the examination, and the medical assessment. Telemedicine relies on information systems with massive storage, and requires the precise operation of tasks by high-performance computing and communications (HPCC) strategies [18]. Researchers used next-generation AI and robot core technologies to simulate the human brain [19; 20]. To control the spread of serious communicable diseases, integrating epidemiology with the rapid computing power of MPCs was proposed [21]. Deep-learning models were implemented on MPCs as accelerators to speed-up medical applications for large biomedical databases [22]. Cambridge-1 was used for building and hosting powerful AI language models in molecular chemistry [23]. Supercomputers have also contributed to everyday activities such as fraudulent insurance claim detection by analyzing massive claim data within a short period of time [24]. The performance of MPCs continues to increase in order to provide the massive computational power required for various applications. According to the TOP500 organization, the first true exascale supercomputer was the Frontier system at the Oak Ridge National Laboratory (ORNL) in the U.S. [25]. It achieved the top rank in November 2022, with a performance exceeding 1.102 Exaflop /s. MPCs can support high accuracy applications because they are driven by matrix multiplication [26].