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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.

SUBMITTER: Ferdinandy B

PROVIDER: S-EPMC7521077 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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

Ferdinandy Bence B Guerrero-Higueras Ángel Manuel ÁM Verderber Éva É Rodríguez-Lera Francisco Javier FJ Miklósi Ádám Á

The Journal of supercomputing 20200928 5

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 k ...[more]

PMID: 33012984

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Project description:BackgroundCanadian funding agencies are no longer content to support research that solely advances scientific knowledge, and key directives are now in place to promote research transfer to policy- and decision-makers. Therefore, it is necessary to improve our understanding of how researchers are trained and supported to facilitate knowledge translation activities. In this study, we investigated differences in health researcher characteristics and knowledge translation activities.MethodsOur sample consisted of 240 health researchers from three Alberta universities. Respondents were classified by research domain [basic (n = 72) or applied (n = 168)] and faculty [medical school (n = 128) or other health science (n = 112)]. We examined our findings using Mode I and Mode II archetypes of knowledge production, which allowed us to consider the scholarly and social contexts of knowledge production and translation.ResultsDifferences among health researcher professional characteristics were not statistically significant. There was a significant gender difference in the applied researcher faculty group, which was predominantly female (p < .05). Research domain was linked to translation activities. Applied researchers reported engaging in significantly more Mode II activities than basic researchers (p < .001), and scored higher than basic researchers regarding the perceived importance of translation activities (Mode I, p = .01; Mode II, p < .001). Main effects of faculty were limited to engaged dissemination (medical school < other faculties; p = .025) and number of publications (medical school > other faculties; p = .004). There was an interaction effect for research domain and faculty group for number of publications (p = .01), in that applied researchers in medical faculties published more than their peers in other faculty groups.ConclusionOur findings illustrate important differences between health researchers and provide beginning insights into their professional characteristics and engagement in Mode I and Mode II activities. A future study designed to examine these dimensions in greater detail, including potential covariates across more varied institutions, would yield richer insights and enable an examination of relative influences, needs and costs of each mode of activity.

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

Publications

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

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