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Accelerating GAN training using highly parallel hardware on public cloud

<!--HTML-->With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and eficient R&D. This work explores different types of cloud services to train a Generative Adversarial Network...

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Autor principal: Da Costa Cardoso, Renato Paulo
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:http://cds.cern.ch/record/2767302
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author Da Costa Cardoso, Renato Paulo
author_facet Da Costa Cardoso, Renato Paulo
author_sort Da Costa Cardoso, Renato Paulo
collection CERN
description <!--HTML-->With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and eficient R&D. This work explores different types of cloud services to train a Generative Adversarial Network (GAN) in a parallel environment, using Tensorflow data parallel strategy. More specifically, we parallelize the training process on multiple GPUs and Google Tensor Processing Units (TPU) and we compare two algorithms: the TensorFlow built-in logic and a custom loop, optimised to have higher control of the elements assigned to each GPU worker or TPU core. The quality of the generated data is compared to Monte Carlo simulation. Linear speed-up of the training process is obtained, while retaining most of the performance in terms of physics results. Additionally, we benchmark the aforementioned approaches, at scale, over multiple GPU nodes, deploying the training process on different public cloud providers, seeking for overall eficiency and cost-effectiveness. The combination of data science, cloud deployment options and associated economics allows to burst out heterogeneously, exploring the full potential of cloud-based services.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2021
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spelling cern-27673022022-11-02T22:25:36Zhttp://cds.cern.ch/record/2767302engDa Costa Cardoso, Renato PauloAccelerating GAN training using highly parallel hardware on public cloud25th International Conference on Computing in High Energy & Nuclear PhysicsConferences<!--HTML-->With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and eficient R&D. This work explores different types of cloud services to train a Generative Adversarial Network (GAN) in a parallel environment, using Tensorflow data parallel strategy. More specifically, we parallelize the training process on multiple GPUs and Google Tensor Processing Units (TPU) and we compare two algorithms: the TensorFlow built-in logic and a custom loop, optimised to have higher control of the elements assigned to each GPU worker or TPU core. The quality of the generated data is compared to Monte Carlo simulation. Linear speed-up of the training process is obtained, while retaining most of the performance in terms of physics results. Additionally, we benchmark the aforementioned approaches, at scale, over multiple GPU nodes, deploying the training process on different public cloud providers, seeking for overall eficiency and cost-effectiveness. The combination of data science, cloud deployment options and associated economics allows to burst out heterogeneously, exploring the full potential of cloud-based services.oai:cds.cern.ch:27673022021
spellingShingle Conferences
Da Costa Cardoso, Renato Paulo
Accelerating GAN training using highly parallel hardware on public cloud
title Accelerating GAN training using highly parallel hardware on public cloud
title_full Accelerating GAN training using highly parallel hardware on public cloud
title_fullStr Accelerating GAN training using highly parallel hardware on public cloud
title_full_unstemmed Accelerating GAN training using highly parallel hardware on public cloud
title_short Accelerating GAN training using highly parallel hardware on public cloud
title_sort accelerating gan training using highly parallel hardware on public cloud
topic Conferences
url http://cds.cern.ch/record/2767302
work_keys_str_mv AT dacostacardosorenatopaulo acceleratinggantrainingusinghighlyparallelhardwareonpubliccloud
AT dacostacardosorenatopaulo 25thinternationalconferenceoncomputinginhighenergynuclearphysics