Zobrazeno 1 - 10
of 464
pro vyhledávání: '"Valduriez, P."'
Autor:
Liu, Ji, Che, Tianshi, Zhou, Yang, Jin, Ruoming, Dai, Huaiyu, Dou, Dejing, Valduriez, Patrick
Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exchanges between devices and the centralized server in the standard FL p
Externí odkaz:
http://arxiv.org/abs/2312.10935
Autor:
Rosendo, Daniel, Keahey, Kate, Costan, Alexandru, Simonin, Matthieu, Valduriez, Patrick, Antoniu, Gabriel
Publikováno v:
ACM REP '23: ACM Conference on Reproducibility and Replicability, Jun 2023, Santa Cruz, California, United States. pp.62-73
Distributed infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex scientific workflows to be executed across hybrid systems spanning from IoT Edge devices to Clouds, and sometimes to super
Externí odkaz:
http://arxiv.org/abs/2307.12796
Autor:
Rosendo, Daniel, Mattoso, Marta, Costan, Alexandru, Souza, Renan, Pina, Débora, Valduriez, Patrick, Antoniu, Gabriel
Publikováno v:
Cluster 2023 - IEEE International Conference on Cluster Computing, Oct 2023, Santa Fe, New Mexico, United States
Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continuum) to enable their optimized execution. Understanding and optimizing
Externí odkaz:
http://arxiv.org/abs/2307.10658
Autor:
Liu, Ji, Dong, Daxiang, Wang, Xi, Qin, An, Li, Xingjian, Valduriez, Patrick, Dou, Dejing, Yu, Dianhai
Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which exceed the capacity of small devices for inference and incurs long train
Externí odkaz:
http://arxiv.org/abs/2207.06667
Publikováno v:
Journal of Parallel and Distributed Computing, Elsevier, 2022, 166, pp.71-94
The explosion of data volumes generated by an increasing number of applications is strongly impacting the evolution of distributed digital infrastructures for data analytics and machine learning (ML). While data analytics used to be mainly performed
Externí odkaz:
http://arxiv.org/abs/2205.01081
Publikováno v:
Conf{\'e}rence sur la Gestion de Donn{\'e}es -- Principles, Technologies et Applications, Oct 2021, Paris, France
Distributed digital infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed from IoT Edge devices to the HPC Cloud (aka the Computing Continuum, the Digital Conti
Externí odkaz:
http://arxiv.org/abs/2109.01379
Autor:
Rosendo, Daniel, Costan, Alexandru, Antoniu, Gabriel, Simonin, Matthieu, Lombardo, Jean-Christophe, Joly, Alexis, Valduriez, Patrick
Publikováno v:
Cluster 2021 - IEEE International Conference on Cluster Computing, Sep 2021, Portland, OR, United States
In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infrastructure with IoT devices interconnected to cloud/HPC systems (aka Co
Externí odkaz:
http://arxiv.org/abs/2108.04033
Autor:
Raphael Saldanha, Reza Akbarinia, Marcel Pedroso, Victor Ribeiro, Carlos Cardoso, Eduardo H. M. Pena, Patrick Valduriez, Fabio Porto
Publikováno v:
Environmental Data Science, Vol 3 (2024)
Climate trends and weather indicators are used in several research fields due to their importance in statistical modeling, frequently used as covariates. Usually, climate indicators are available as grid files with different spatial and time resoluti
Externí odkaz:
https://doaj.org/article/f6b4f0b4f303466c82f38f3431fe559a
Autor:
Souza, Renan, Silva, Vítor, Lima, Alexandre A. B., de Oliveira, Daniel, Valduriez, Patrick, Mattoso, Marta
Complex scientific experiments from various domains are typically modeled as workflows and executed on large-scale machines using a Parallel Workflow Management System (WMS). Since such executions usually last for hours or days, some WMSs provide use
Externí odkaz:
http://arxiv.org/abs/2105.04720
Deep learning models have become increasingly useful in many different industries. On the domain of image classification, convolutional neural networks proved the ability to learn robust features for the closed set problem, as shown in many different
Externí odkaz:
http://arxiv.org/abs/2102.03243