Exploring Simplicity and Efficiency: Regression-based Scheduling Heuristics in HPC

  • Lucas Rosa USP
  • Danilo Carastan-Santos CNRS / Inria / Grenoble INP / Univ. Grenoble Alpes
  • Alfredo Goldman USP


This research examines the interplay between resource management in high-performance computing systems and the application of machine learning techniques in developing scheduling heuristics. The potential for improved performance, through scheduling heuristics based on linear regression and polynomial job characteristics, was explored. Larger polynomials caused instability due to multicollinearity effects, but the simplest polynomial delivered stable and efficient scheduling performance. The study also evaluates the longterm resilience of these regression-based heuristics.


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ROSA, Lucas; CARASTAN-SANTOS, Danilo; GOLDMAN, Alfredo. Exploring Simplicity and Efficiency: Regression-based Scheduling Heuristics in HPC. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DE SÃO PAULO (ERAD-SP), 14. , 2023, São José dos Campos/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 41-44. DOI: https://doi.org/10.5753/eradsp.2023.232635.

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