As computing becomes substantial for achieving scientific and social progress, its environmental
implications often remain underestimated. While the value of scientific computing is witnessed
by its ubiquitous achievements, its growing demands have lead, in turn, to increased energy
and carbon footprint costs. With the goal of describing such computational trace in subnuclear
physics (SNP), this work estimates the energy consumption of benchmark SNP workloads with a
containerized original monitoring software. The benchmark workloads used in this work are GEN-
SIM, DIGI and RECO containerized jobs deployed by the HEPScore project. The monitoring
software extracts the CPU and RAM usage of such jobs in real-time via process IDs and estimates,
with this information, their energy (kWh) and carbon utilization (gCO2e). The results can be
used as a starting point towards a “greener” approach to computing methods and integrate current
benchmarking scores with energy efficiency-related metrics.