1,721,068 research outputs found
The Environmental Cost of High Performance Computing System Simulation
No full textIn this research paper, we explore the essential role of High Performance Computing (HPC) in the current techno-logical era, highlighting its extensive use in various sectors, while also considering growing alarm over its environmental footprint. High performance computing systems are essential for managing large data sets and solving complex challenges. However, their significant contribution to escalating energy consumption and carbon emissions in the information and communication technology (ICT) sector cannot be ignored. Our study identifies the increasing energy demands and environmental challenges associated with HPC activities, including resource use, electrical waste and greenhouse gas emissions. It highlights the importance of understanding these environmental impacts in detail. It also contributes to the ongoing dialogue on sustainable computing, promoting a harmonious future where technological progress and environmental sustainability can coexist in unison
Sustainability and High Performance Computing
No full textThe use of High Performance Computing (HPC) has become a fundamental aspect of scientific research, industrial innovation and complex data analytics. However, the growing demand for faster and more powerful computational capabilities has resulted in a significant increase in energy consumption and associated environmental impacts. This paper aims to investigate the relationship between sustainability and HPC, with the objective of identifying methodologies that can make HPC systems more environmentally friendly without compromising their performance
Machine Learning for KPI Development in Public Administration
No full textEfficient and effective service delivery to citizens in Public Administrations (PA) requires the use of key performance indicators (KPIs) for performance evaluation and measurement. This paper proposes an innovative framework for constructing KPIs in performance evaluation systems using Random Forest and variable importance analysis. Our approach aims to identify the variables that have a strong impact on the performance of PAs. This identification enables a deeper understanding of the factors that are critical for organizational performance. By analyzing the importance of variables and consulting domain experts, relevant KPIs can be developed. This ensures improvement strategies focus on critical aspects linked to performance. The framework provides a continuous monitoring flow for KPIs and a set of phases for adapting KPIs in response to changing administrative dynamics. The objective of this study is to enhance the performance of PAs by applying machine learning techniques to achieve a more agile and results-oriented PAs
On the interpretation of traces of low level events in business process logs Extended abstract
No full textData Mining for Animal Health to Improve Human Quality of Life: Insights from a University Veterinary Hospital
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