Sustainable Supercomputing: Energy Efficiency and Resource Management through Statistical Modeling and Machine Learning

• Grant number: 23/09048-8
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: September 01, 2024
• End date: July 31, 2028
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Alfredo Goldman vel Lejbman
• Grantee: Lucas de Sousa Rosa
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 19/26702-8 - Trends on high performance computing, from resource management to new computer architectures, AP.TEM

Abstract

This PhD research project addresses challenges in resource management and workload modeling in the field of supercomputing. The proposed strategy includes the use of automated techniques, such as machine learning and optimization based on design of experiments, taking into account energy aspects of tasks and platforms. We will carry out scheduling experiments via simulations, aiming to generate data on scheduling behavior in different scenarios, evaluate performance and energy efficiency metrics, as well as compare various methodologies. The project also aims to investigate an underexplored aspect of workload modeling: the representation of tasks' energy consumption. Finally, we intend to test these more sophisticated models by evaluating their representativeness.