# Investigating Supercomputer Performance with Sustainability in the Era of Artificial Intelligence
## 3 Literature Review
### Related Works  
In this section, the article presents only the most recent and highly relevant works to situate the current study within the existing literature. Also, insight into the differences between the current study and previously published works is provided. For example, the energy performance of the Eurora supercomputer (top-ranked in the 2013 Green500) was analyzed under different workloads. The findings indicated that variability significantly impacts EE [30]. However, this study primarily focused on a single supercomputer and its EE. In another study, real-time power demand data for the Frontier supercomputer, along with waste heat measurements from the HPC center, were collected over the course of one year in 2023. The data were reported to guide research on waste heat optimization [31]. However, the work by [PERSON] et al. [31] is limited to Frontier and one year of real-time data. Therefore, it is limited to one supercomputer and does not account for long-term variability. Similarly, the power consumption levels of the Cori and Perlmutter supercomputers were compared. The analysis revealed that power usage within the same application domain is consistent and that power consumption levels for the same application run by different users are similar [32]. However, the findings by [PERSON] et al. [32] are limited to Cori and Perlmutter and do not address performance metrics. Similarly, a study analyzed the trends in computational nodes of exascale supercomputers by examining their architecture and key technologies. The study highlights the EE challenges faced by exascale supercomputers [33]. The study examines EE in isolation without considering sustainability. An algorithm was developed to estimate carbon dioxide emissions from large-scale computations without interfering with the machine's source code while accounting for hardware configurations. The goal was to create carbon awareness and provide recommendations to minimize carbon emissions, thereby promoting green computing [15]. However, the study is limited to carbon awareness and does not consider the interplay between carbon dioxide emissions, power consumption, and performance. Another study analyzedthe 59 th edition of TOP500, focusing on Rmax, Rpeak, and high-performance conjugate gradient metrics. The study examined energy demands driven by increasing AI operations and found that AI workloads are computationally expensive, with EE slowing geometrically due to scaling [34]. However, this study is limited to the 59 th edition of TOP500 and, therefore, cannot provide trends across multiple years. Unlike the work of [PERSON] et al. [15], a separate study conducted power consumption and performance analysis of legacy applications in the scientific domain for a single two-socket HPC node. It found that energy consumption increases as molecular size grows [35]. Similarly, [PERSON] et al. [36] explored the decarbonization of HPC centers, while [PERSON] et al. [37] focused on performance comparisons between unified memory and explicit memory management, providing insights into challenges faced by performance. A study was conducted on the carbon footprint of HPC systems with a focus on sustainability within the region where an HPC system is located. The study modeled the quantification of the carbon footprint within the region [38]. Another study measured EE in data centers and proposed a renewable framework for sustainable energy efficiency. The investigation revealed that the proposed framework reduced energy consumption [39]. However, both studies [38; 39] overlooked the interplay between carbon footprint, EE, and performance over long period of time. A study investigated instantaneous power consumption and EE in future supercomputers, taking coding optimization as a case study. The study demonstrated the impact of coding optimization on different computational stages [40]. Another study evaluated four supercomputers based on the HPCC benchmark rather than limiting performance evaluation to the Linpack benchmark.