## 4 Materials and Methods
### Computing Environment  
The execution of the four workloads was performed on a DGX A100 machine equipped with 8 \(\times\) Nvidia A100 GPUs per node, provided by CINECA (Consorzio Interuniversitario dell'Italia Nord Est per il Calcolo Automatico), a university consortium that supports research activities through the use of supercomputers. Specifically, these computational resources were made available through ISCRA (Italian SuperComputing Resource Allocation), a project within CINECA that allocates access to their machines based on different classes tailored to user needs. The trainings were launched as jobs through the SLURM scheduler, using batch mode. This non-interactive mode allows for the use of GPUs and all requested resources, with jobs starting once the necessary resources become available. During execution, energy consumption of CPU, GPU, and RAM was tracked using the CodeCarbon library. Specifically, energy consumption in kWh was recorded every 5 min. These trainings were conducted on this machine to take advantage of the computational power of the GPUs.  
One of the advantages of the CodeCarbon library is its ability to track energy consumption specific to the designated process rather than the entire machine. This is important because the CINECA machine supports multiple users running jobs simultaneously, which could otherwise distort the estimated power consumption. Since this work focuses solely on operational emissions, the power consumption data obtained was mapped against historical data of local marginal emissions. In this context, our organisation is a general member of the Green Software Foundation and has established a research partnership with WattTime, which provided historical data (Figure 3) for various regions dating back to 2021. For this work, results were evaluated for seven regions in Europe: Italy, Germany, France, the United Kingdom, Ireland, Spain, and Sweden. Additionally, Northern Carolina was used as a reference region for non-region-specific runs. WattTime provides data for this region for preview purposes, and several experiments available online use Northern Carolina as a testbed (e.g., [45; 46; 47; 48]).  
The selection of the European regions is due to three main reasons:  
* Compliance with privacy policies: The General Data Protection Regulation (GDPR) is a regulation of the European Union regarding the processing of personal data and privacy. It stipulates that personal data can only be transferred to countries or organisations that provide an adequate level of data protection.
* Access to regions that use both renewable and non-renewable energy sources: this allows for a diverse analysis of emissions across different energy profiles.
* Availability of specific cities from the list of AWS Cloud instances: these regions provide a credible testbed for experiments.  
Regarding the datasets, they consist of.csv files with several features, each containing marginal emission information for a specific region over a month. The features include the following:  
1. Timestamp: This represents the date and time of each collected sample. As previously mentioned, the data was recorded with a granularity of 5 min.
2. MOER: Marginal Operating Emission Rate, which refers to the rate of carbon emissions emitted per unit of consumed energy, expressed in lbs/kWh. This value has been converted because the power consumption was tracked in kWh, and the carbon emissions were provided in gCO\({}_{2}\)eq (grammes of carbon dioxide equivalent).  
Figure 3: An example of a dataset provided by WattTime. In this case, it is the.csv file for the Italian region.  
MOER version: refers to the version of the provided MOER data.
* Frequency: the duration in seconds for which the data is valid from point_time, where point_time is the date/time format indicating when this data became valid.