Titlebook: Job Scheduling Strategies for Parallel Processing; 25th International W Dalibor Klusá?ek,Corbalán Julita,Gonzalo P. Rodrig Conference proce

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AI-Job Scheduling on?Systems with?Renewable Power Sourceslead to increased power consumption of data centers. Even when considering improved operational efficiency, the conservative estimate of the power requirement for data centers in the year 2030 stands at 900 TWh. To comply with present political requirements, renewable energy sources must provide mos

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Toward Building a?Digital Twin of?Job Scheduling and?Power Management on?an?HPC System systems and thereby helping them properly configure the system parameters and policies. Specifically, this paper focuses on a real computing system, named Supercomputer AOBA, as an example to discuss the importance of accurately reproducing the behaviors of job scheduling in the simulation. Since A

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Encoding for?Reinforcement Learning Driven Schedulingven scheduling is state representation for RL agent (i.e., capturing essential features of dynamic scheduling environment for decision making). Existing state encoding approaches either lack critical scheduling information or suffer from poor scalability. In this study, we present SEM (Scalable and

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RADICAL-Pilot and?PMIx/PRRTE: Executing Heterogeneous Workloads at?Large Scale on?Partitioned HPC Ren challenges for runtime systems. Task heterogeneity increases the complexity of resource and execution management, limiting the scalability and efficiency of workflow execution. Resource partitioning and distribution of tasks execution over portioned resources promises to address those problems but

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RARE: Renewable Energy Aware Resource Management in?Datacentersinable solutions to pressing energy and digital infrastructure challenges is crucial. Several hyperscale cloud providers have announced plans to power their datacenters using renewable energy. However, integrating renewables to power the datacenters is challenging because the power generation is int

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