Open Access
| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01130 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701130 | |
| Published online | 07 October 2025 | |
- WLCG, The worldwide lhc computing grid, https://wlcg-public.web.cern.ch [Google Scholar]
- M. Horzela, H. Casanova et al., Modeling Distributed Computing Infrastructures for HEP Applications, EPJ Web of Conf. 295, 04032 (2024). 10.1051/epjconf/202429504032 [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
- M. Horzela, Measurement of Triple-Differential Z+Jet Cross Sections with the CMS Detector at 13 TeV and Modelling of Large-Scale Distributed Computing Systems (2023). 10.5445/IR/1000165566 [Google Scholar]
- H. Casanova, A. Giersch et al., Versatile, scalable, and accurate simulation of distributed applications and platforms, Journal of Parallel and Distributed Computing 74, 2899 (2014). 10.1016/j.jpdc.2014.06.008 [Google Scholar]
- H. Casanova, R. Ferreira da Silva et al., Developing Accurate and Scalable Simulators of Production Workflow Management Systems with WRENCH, Future Generation Computer Systems 112, 162 (2020). 10.1016/j.future.2020.05.030 [CrossRef] [Google Scholar]
- P. Velho, L.M. Schnorr et al., On the Validity of Flow-Level Tcp Network Models for Grid and Cloud Simulations, ACM TOMACS 23 (2013). 10.1145/2517448 [Google Scholar]
- P. Velho, A. Legrand, Accuracy Study and Improvement of Network Simulation in the SimGrid Framework (2009). 10.4108/ICST.SIMUTOOLS2009.5592 [Google Scholar]
- K. Fujiwara, H. Casanova, Speed and Accuracy of Network Simulation in the SimGrid Framework (2007), https://dl.acm.org/doi/10.5555/1345263.1345279 [Google Scholar]
- A. Lèbre, A. Legrand et al., Adding Storage Simulation Capacities to the SimGrid Toolkit: Concepts, Models, and API (2015). 10.1109/CCGrid.2015.134 [Google Scholar]
- L. Stanisic, E. Agullo et al., Fast and Accurate Simulation of Multithreaded Sparse Linear Algebra Solvers (2015). 10.1109/ICPADS.2015.67 [Google Scholar]
- T. Cornebize, A. Legrand, F.C. Heinrich, Fast and Faithful Performance Prediction of MPI Applications: the HPL Case Study (2019). 10.1109/CLUSTER.2019.8891011 [Google Scholar]
- H. Salehinejad et al., Recent Advances in Recurrent Neural Networks (2018). 10.48550/ARXIV.1801.01078 [Google Scholar]
- Y. Duan, Y. Liu et al., Improved BIGRU Model and Its Application in Stock Price Forecasting, Electronics 12, 2718 (2023). 10.3390/electronics12122718 [Google Scholar]
- K.A. Althelaya, E.S.M. El-Alfy, S. Mohammed, Evaluation of bidirectional LSTM for short-and long-term stock market prediction (2018). 10.1109/IACS.2018.8355458 [Google Scholar]
- N. Wu, B. Green et al., Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case (2020). 10.48550/ARXIV.2001.08317 [Google Scholar]
- V. Zhyla, Performance modeling of distributed computing (2024). 10.5445/IR/1000175096 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

