Sustainable AI: Experiences, Challenges & Recommendations

The use of Artificial Intelligence (AI) and Machine Learning (ML) as part of scientific workloads is becoming increasingly widespread. It is imperative to understand how to configure AI and ML applications on HPC systems to optimise their performance and energy efficiency, thereby minimising their environmental impact. In this study, we use MLPerf HPC’s DeepCAM benchmark to assess and explore the energy efficiency of ML applications on different hardware platforms. We highlight the challenges that, despite growing popularity, ML frameworks still present in a traditional HPC environment, as well as the challenges of measuring power and energy on a variety of HPC and cloud-like virtualised systems. We conclude our study by proposing recommendations that will improve and encourage best practices around sustainable AI and ML workloads on HPC
systems.