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BestOf: an online implementation selector for the training and inference of deep neural networks

Barrachina, Sergio ; Castelló, Adrián ; Dolz, Manuel F ; Tomás Domínguez, Andrés Enrique

Springer-Verlag 2022-05

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  • Título:
    BestOf: an online implementation selector for the training and inference of deep neural networks
  • Autor: Barrachina, Sergio ; Castelló, Adrián ; Dolz, Manuel F ; Tomás Domínguez, Andrés Enrique
  • Assuntos: ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES ; Auto-tuning ; Deep neural networks ; Implementation selector ; Python
  • Notas: info:eu-repo/grantAgreement/GVA//CDEIGENT%2F2018%2F014//Plan GenT/
    info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113656RB-C22/ES/COMPUTACION Y COMUNICACIONES DE ALTAS PRESTACIONES CONSCIENTES DEL CONSUMO ENERGETICO. APLICACIONES AL APRENDIZAJE PROFUNDO COMPUTACIONAL - UPV/
    info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//FJC2019-039222-I//AYUDA JUAN DE LA CIERVA FORMACION-CASTELLO GIMENO, ADRIAN/
    https://doi.org/10.1007/s11227-022-04577-2
    The Journal of Supercomputing
  • Descrição: [EN] Tuning and optimising the operations executed in deep learning frameworks is a fundamental task in accelerating the processing of deep neural networks (DNNs). However, this optimisation usually requires extensive manual efforts in order to obtain the best performance for each combination of tensor input size, layer type, and hardware platform. In this work, we present BestOf, a novel online auto-tuner that optimises the training and inference phases of DNNs. BestOf automatically selects at run time, and among the provided alternatives, the best performing implementation in each layer according to gathered profiling data. The evaluation of BestOf is performed on multi-core architectures for different DNNs using PyDTNN, a lightweight library for distributed training and inference. The experimental results reveal that the BestOf auto-tuner delivers the same or higher performance than that achieved using a static selection approach. This research was funded by Project PID2020-113656RB-C21/C22 supported by MCIN/AEI/10.13039/501100011033. Manuel F. Dolz was also supported by the Plan Gen-T grant CDEIGENT/2018/014 of the Generalitat Valenciana. Adrian Castello is a FJC2019-039222-I fellow supported by MCIN/AEI/ 10.13039/501100011033. Barrachina, S.; Castelló, A.; Dolz, MF.; Tomás Domínguez, AE. (2022). BestOf: an online implementation selector for the training and inference of deep neural networks. The Journal of Supercomputing. 78(16):17543-17558. https://doi.org/10.1007/s11227-022-04577-2
  • Editor: Springer-Verlag
  • Data de criação/publicação: 2022-05
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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