Zobrazeno 1 - 10
of 292
pro vyhledávání: '"Aldinucci Marco"'
Autor:
Aldinucci, Marco, Danelutto, Marco
Publikováno v:
Proc. of PDCS: Intl. Conference on Parallel and Distributed Computing and Systems, pages 955-962, Cambridge, Massachusetts, USA, Nov. 1999. IASTED, ACTA press
We discuss the properties of the composition of stream parallel skeletons such as pipelines and farms. By looking at the ideal performance figures assumed to hold for these skeletons, we show that any stream parallel skeleton composition can always b
Externí odkaz:
http://arxiv.org/abs/2408.12394
In large distributed systems, failures are a daily event occurring frequently, especially with growing numbers of computation tasks and locations on which they are deployed. The advantage of representing an application with a workflow is the possibil
Externí odkaz:
http://arxiv.org/abs/2407.05337
Autor:
Colonnelli, Iacopo, Medić, Doriana, Mulone, Alberto, Bono, Viviana, Padovani, Luca, Aldinucci, Marco
Publikováno v:
Formal Methods. FM 2024. Lecture Notes in Computer Science, vol 14933. Springer, Cham
In the ever-evolving landscape of scientific computing, properly supporting the modularity and complexity of modern scientific applications requires new approaches to workflow execution, like seamless interoperability between different workflow syste
Externí odkaz:
http://arxiv.org/abs/2407.01838
Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several labels (classes). When encountering domain shifts, e.g., classifier of movie reviews from IMDb to Rotten
Externí odkaz:
http://arxiv.org/abs/2405.01883
Autor:
Cesare, Valentina, Becciani, Ugo, Vecchiato, Alberto, Lattanzi, Mario Gilberto, Pitari, Fabio, Aldinucci, Marco, Bucciarelli, Beatrice
Publikováno v:
PASP, 135, 074504 (2023)
We ported to the GPU with CUDA the Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) Parallel Solver developed for the ESA Gaia mission, by optimizing a previous OpenACC porting of this application. The code aims to find, with a [1
Externí odkaz:
http://arxiv.org/abs/2308.00778
Publikováno v:
CEUR Workshop Proceedings Vol. 3340, pp. 99-110, (2022)
Classic Machine Learning techniques require training on data available in a single data lake. However, aggregating data from different owners is not always convenient for different reasons, including security, privacy and secrecy. Data carry a value
Externí odkaz:
http://arxiv.org/abs/2303.17942
Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a method for privacy-preserving pooling of datasets employing collaborative t
Externí odkaz:
http://arxiv.org/abs/2303.10630
Publikováno v:
In Euro-Par 2023: Parallel Processing. Euro-Par 2023. Lecture Notes in Computer Science, vol 14100. Springer, Cham
Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scen
Externí odkaz:
http://arxiv.org/abs/2303.04906
Publikováno v:
In Companion Proceedings of the ACM Web Conference 2023 (pp. 1177-1181)
Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the a
Externí odkaz:
http://arxiv.org/abs/2302.07684