Open AccessPredicting Runtime in HPC Environments for an Efficient Use of Computational Resources
Author(s) -
Mariza Ferro,
Vinícius Klôh,
Matheus Gritz,
Vitor de Sá,
Bruno Schulze
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wscad.2021.18513
Subject(s) - computer science , supercomputer , computational complexity theory , decision tree , artificial neural network , computational model , machine learning , tree (set theory) , distributed computing , computer engineering , artificial intelligence , computer architecture , parallel computing , algorithm , mathematical analysis , mathematics
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.