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This paper extends the validation of QRMI to a broad range of workload managers, including PBS, LSF, Grid Engine, Kubernetes, and the Flux Framework, encompassing traditional batch schedulers, a cloud-native orchestration platform, and a graph-based scheduler.
Modern High Performance Computing (HPC) workloads exchange datasets at high velocity to enable data-driven science. HPC systems employ hardware and software I/O accelerators to enable efficient data exchange. However, domain scientists and software engineers must manually request specific hardware accelerators and manu...
This work assesses the reliability of a dispatch agent across 432 runs, and finds that descriptive metadata increases successful execution from 48% to 87% of 220 submitted jobs, eliminating architecture mismatch, and improving performance for five of ten measurable applications by up to 3.3x.
Fluence, a Kubernetes scheduler plugin backed by the Fluxion graph-based scheduler, enabling gang-scheduled placement for quantum-classical workloads and custom resources and shows that quantum-awareness can be added to a cloud-native scheduler without modifying user containers.
The extent to which an agentic framework can optimize and run an HPC scaling study with a low latency network in Amazon Web Services, accurately transform HPC job specifications between workload managers, and design and run an entire biosciences workflow is assessed.
This work design agents to intelligently deliver the entire life-cycle of an HPC application experimental run in cloud -- creation and build of a container, deployment in Kubernetes, optimization, and orchestration of a scaling study.
This paper presents a hierarchical, dynamic architecture and software to discover resources across diverse cloud, edge, and HPC systems and exemplifies the importance of careful coordination between agents, discovery tools, and infrastructure for agentic science.
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