Abstract
To reach the new milestone in High Performance Computing, energy and power constraints have to be considered. Optimal workload distributions are necessary in heterogeneous architectures to avoid inefficient usage of computational resources. Static load balancing techniques are not able to provide optimal workload distributions for problems of irregular nature. On the other hand, dynamic load balancing algorithms are coerced by energy metrics that are usually slow and difficult to obtain. We present a methodology based on Machine Learning to perform dynamic load balancing in iterative problems. Machine Learning models are trained using data acquired during previous executions. We compare this new approach to two dynamic workload balancing techniques already proven in the literature. Inference times for the Machine Learning models are fast enough to be applied in this environment. These new predictive models further improve the workload distribution, reducing the energy consumption of iterative problems.
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Acknowledgments
This work was supported by the Spanish Ministry of Education and Science through the TIN2016-78919-R project, the Government of the Canary Islands, with the project ProID2017010130 and the grant TESIS2017010134, which is co-financed by the Ministry of Economy, Industry, Commerce and Knowledge of Canary Islands and the European Social Funds (ESF), operative program integrated of Canary Islands 2014–2020 Strategy Aim 3, Priority Topic 74 (85%); the Spanish network CAPAP-H.
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Cabrera, A., Almeida, F., Blanco, V., Castellanos–Nieves, D. (2020). Improving Energy Consumption in Iterative Problems Using Machine Learning. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2019. Lecture Notes in Computer Science(), vol 12044. Springer, Cham. https://doi.org/10.1007/978-3-030-43222-5_12
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