# Investigating Supercomputer Performance with Sustainability in the Era of Artificial Intelligence
## 1 Introduction
Alphabet, the parent company of Google, has invested a substantial amount in AI. In the last quarter of 2023, IBM pumped USD 500 million into enterprise AI projects. In the second quarter of 2024, CISCO injected USD 1 billion as a fund for AI investment. Out of the USD 1 billion, USD 200 million was allocated for investment in generative AI in AI companies such as Mistral AI, Coherence, and Scale AI. Microsoft pledges to inject USD 3.2 billion over a period of three years. In addition, Microsoft backed OpenAI with an investment of USD 13 billion, leading to the integration of ChatGPT into the Bing search engine. Sapphire Ventures has announced its investment of USD 1 billion to support startups focusing on AI technology [8]. The significant investments in AI, amounting to hundreds of billions of US dollars, made by companies have the potential to significantly increase demand for faster supercomputers, scale up supercomputer operations, and exacerbate power consumption issues. This, in turn, could lead to higher carbon dioxide emissions, particularly if the power sourcesare not renewable. However, giant companies like Amazon, Meta, Google, Microsoft, and Apple have pledged to power their data centers with renewable energy [9; 10]. As mentioned previously, the world has entered the era of exascale supercomputing and generative AI [11], thus drawing significant attention to sustainability [12]. Supercomputers contribute to carbon dioxide footprints, particularly in regions where those datacenters are located [13], with HPC applications like large-scale modeling adding to emissions [14]. The carbon dioxide footprint from computation is substantial and poses a threat to the climate [15], with HPC and data centers emitting approximately 100 megatonnes of carbon dioxide annually. This is significantly contributing to climate change [5]. By 2030, the carbon footprint of the IT industry is projected to increase ten-fold, with power consumption estimated at 200 TWh per year [5]. Supercomputers emit greenhouse gases [16], whose atmospheric concentration profoundly influences climate change. The consequences of climate change include rising sea levels, severe wildfires (e.g., in Australia), destructive typhoons (e.g., in the Pacific), devastating droughts (e.g., in Africa), and adverse impacts on human health [5]. The growing demand for faster supercomputers with powerful computational capabilities has significantly increased power consumption and environmental impact [16]. Therefore, it is critical for the scientific community to consider both environmental impact and large-scale computational operations running on supercomputers [17]. The rapid development in machine learning poses a challenge for the environment because model pre-training requires extensive computational resources, materials, and energy [7]. This creates a challenge in balancing supercomputer performance with sustainability, resulting in the creation of a multi-objective optimization problem. Many studies have analyzed the performance, power consumption, and carbon footprint of supercomputers. However, most research has extensively examined the issues of energy efficiency and performance in isolation. Only limited studies have attempted to explore the relationship between performance and energy efficiency (EE), and these are typically restricted to one or two supercomputers within a given year. Such studies often fail to provide insights into trends over time. Another issue is that previous research has overlooked the behavior of groups of top-performing supercomputers and top-energy-efficiency supercomputers in terms of performance and sustainability simultaneously over time. The relationship between the top-performing and most energy-efficient supercomputers has frequently been ignored in past studies. This paper's intent is to comprehensively analyze the interplay between performance and EE in supercomputers by examining trends over year-over-year periods. Also, the study examines the behavior of top-performing and top-EE supercomputers.