# Investigating Supercomputer Performance with Sustainability in the Era of Artificial Intelligence
## 5 Results and Discussion
### Top-Performing Supercomputers' Power Consumption  
Table 1 presents the analysis of the power (Kw) consumption of top-performing supercomputers. Note that the data for some supercomputers were not released at the time of the data collection, which explains why the \"N\" column contains values below 10 for certain periods. The minimum (Min) column indicates the lowest power consumption among the top-performing supercomputers. Observing the Min column reveals an initial increase in power consumption, which stabilizes at 1764 and then reduces to 921 before increasing again, with the exception of a drop in Nov24. Similarly, the maximum (Max) power consumption shows an increase in Nov20, stabilizes for a period, and then rises again before stabilizing in June24 and Nov24.  
In general, both the Min and Max power consumption show an upward trend over the five-year period under investigation. The mean power consumption consistently increases throughout the period, indicating a growing demand for power among the most powerful supercomputers. The standard deviation remains high across the years. However, June22 shows an increase, followed by a reduction in Nov22 and June23 before another sharp increase. This reflects the wide variability in the capabilities and efficiency of the supercomputers. Periods of stabilization in power consumption may be attributed to the same system being on the lists for 2 or 3 consecutive years.  
Table 2 presents the _correlation_ for the power consumption of top-performing supercomputers. Strong correlations are observed between Nov23 and June24, Nov22 and June23, and June21 and Nov21, indicating robust similarities in power consumption among the top-performing supercomputers during these periods. The correlation result suggests consistency in power consumption, likely because of sustained efforts to improve performance. Moderate _correlations_ (e.g., June22 and Nov23, Nov21 and Nov22) indicate meaningful  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **N** & **Min Power (Kw)** & **Max Power (Kw)** & **Mean** & **Std. Dev.** \\ \hline Nov24 & 9 & 3387 & 38,698 & 17,373.11 & 13,210.231 \\ June24 & 8 & 4159 & 38,698 & 15,679.12 & 13,076.048 \\ Nov23 & 8 & 2560 & 29,899 & 13,986.75 & 10,162.440 \\ June23 & 10 & 2589 & 29,899 & 12,264.40 & 9098.486 \\ Nov22 & 10 & 2589 & 29,899 & 11,924.70 & 9028.653 \\ June22 & 10 & 921 & 29,899 & 11,148.40 & 9744.542 \\ Nov21 & 9 & 1764 & 29,899 & 10,059.67 & 9618.801 \\ June21 & 9 & 1764 & 29,899 & 10,052.89 & 9624.743 \\ Nov20 & 8 & 1764 & 29,899 & 10,993.50 & 9837.126 \\ June20 & 9 & 1344 & 28,335 & 9686.44 & 9409.253 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical analysis for the supercomputers’ power consumption.  
but weaker relationships. These suggest some diversity in power consumption, reflecting variations in supercomputer configurations among the powerful supercomputers. Weak correlations (e.g., June23 and Nov24, Nov22 and Nov24) highlight weak relationships, likely reflecting significant changes in the list over the period under investigation. Additionally, the weak _correlation_ observed between the earlier periods and later periods clearly suggests an evolution in power consumption trends caused by new entries in the ranked list over the five-year period.