Performance Evaluation for Scientific Applications using the Roofline Model
DOI:
https://doi.org/10.5753/reic.2023.2429Keywords:
Performance Evaluation, HPC, Roofline, Machine Learning, Scientific ApplicationsAbstract
Understanding the factors that limit the performance of applications and their computational requirements can help in software optimization and in the acquisition, development and choice of hardwares that best meet the application's computational performance. This work proposes a methodology for evaluating and characterizing the performance of scientific applications using the Roofline model. A series of experiments with a set of applications were developed and analyzed following the proposed methodology. The results allow us to identify the aspects that limit the performance of applications, suggest software optimizations and the hardware that increases the performance of the applications.
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