Buch, Englisch, Band 14591, 197 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 330 g
27th International Workshop, JSSPP 2024, San Francisco, CA, USA, May 31, 2024, Revised Selected Papers
Buch, Englisch, Band 14591, 197 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 330 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-74429-7
Verlag: Springer Nature Switzerland
This book constitutes the refereed proceedings of the 27th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2024, held in San Francisco, CA, USA, on May 31, 2024.
The 10 full papers included in this book were carefully reviewed and selected from 15 submissions. The JSSPP 2024 covers several interesting problems within the resource management and scheduling domains.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Rechnerarchitektur
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
- Mathematik | Informatik EDV | Informatik Technische Informatik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
Weitere Infos & Material
.- Technical papers.
.- Real-life HPC Workload Trace Featuring Refined Job Runtime Estimates.
.- An Empirical Study of Machine Learning-based Synthetic Job Trace Generation Methods.
.- Clustering Based Job Runtime Prediction for Backfilling Using Classification.
.- Launchpad: Learning to Schedule Using Offline and Online RL Methods.
.- Radical-Cylon: A Heterogeneous Data Pipeline for Scientific Computing.
.- Evaluation of Heuristic Task-to-Thread Mapping Using Static and Dynamic Approaches.
.- Challenges in parallel matrix chain multiplication.
.- A node selection method for on-demand job execution with considering deadline constraints.
.- Maximizing Energy Budget Utilization Using Dynamic Power Cap Control.
.- Run your HPC jobs in Eco-Mode: revealing the potential of user-assisted power capping in supercomputing systems.
