Exploratory study of introducing HPC to non-ICT researchers: institutional strategy is possibly needed for widespread adaption.

Abstract

Machine learning algorithms are becoming more and more useful in many fields of science, including many areas where computational methods are rarely used. High-performance Computing (HPC) is the most powerful solution to get the best results using these algorithms. HPC requires various skills to use. Acquiring this knowledge might be intimidating and take a long time for a researcher with small or no background in information and communications technologies (ICTs), even if the benefits of such knowledge is evident for the researcher. In this work, we aim to assess how a specific method of introducing HPC to such researchers enables them to start using HPC. We gave talks to two groups of non-ICT researchers that introduced basic concepts focusing on the necessary practical steps needed to use HPC on a specific cluster. We also offered hands-on trainings for one of the groups which aimed to guide participants through the first steps of using HPC. Participants filled out questionnaires partly based on Kirkpatrick's training evaluation model before and after the talk, and after the hands-on training. We found that the talk increased participants' self-reported likelihood of using HPC in their future research, but this was not significant for the group where participation was voluntary. On the contrary, very few researchers participated in the hands-on training, and for these participants neither the talk, nor the hands-on training changed their self-reported likelihood of using HPC in their future research. We argue that our findings show that academia and researchers would benefit from an environment that not only expects researchers to train themselves, but provides structural support for acquiring new skills.