Job Scheduling Strategies for Parallel Processing

Job Scheduling Strategies for Parallel Processing

27th International Workshop, JSSPP 2024, San Francisco, CA, USA, May 31, 2024, Revised Selected Papers

Corbalan, Julita; Rodrigo, Gonzalo P.; Klusacek, Dalibor

Springer International Publishing AG

01/2025

203

Mole

9783031744297

Pré-lançamento - envio 15 a 20 dias após a sua edição

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.- 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.
parallel computing;parallel processing;distributed systems;scheduling;job scheduling;VM scheduling;system modeling;workload modeling;system evaluation;workload evaluation;performance evaluation;performance optimization;simulation;cloud computing;grid computing;cluster computing;HPC computing;GPU;resource isolation;workflow