Predicting Runtime in HPC Environments for an Efficient Use of Computational Resources

  • Mariza Ferro LNCC
  • Vinicius P. Klôh LNCC
  • Matheus Gritz LNCC
  • Vitor de Sá LNCC
  • Bruno Schulze LNCC


Understanding the computational impact of scientific applications on computational architectures through runtime should guide the use of computational resources in high-performance computing systems. In this work, we propose an analysis of Machine Learning (ML) algorithms to gather knowledge about the performance of these applications through hardware events and derived performance metrics. Nine NAS benchmarks were executed and the hardware events were collected. These experimental results were used to train a Neural Network, a Decision Tree Regressor and a Linear Regression focusing on predicting the runtime of scientific applications according to the performance metrics.


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FERRO, Mariza; KLÔH, Vinicius P.; GRITZ, Matheus; SÁ, Vitor de; SCHULZE, Bruno. Predicting Runtime in HPC Environments for an Efficient Use of Computational Resources. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (WSCAD), 22. , 2021, Belo Horizonte. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 72-83. DOI: