Evaluates machine learning methods to predict execution time in heterogeneous computing networks, suggesting enhanced resource efficiency.
Authors consider an applicability of machine learning methods to predict task execution time on nodes of high-performance computing devices in order to guarantee an efficient usage of computational resources and therefore optimal planning. The purpose is to find methods to minimize difference between estimated and registered value of the execution time. Authors use system logs with data on computational resource consumptions. Results are presented with estimated dispersion and confidence intervals.
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Piskovski et al. (2026) studied this question.
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