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At least 919 records · Page 51

Towards a Standard Process Management Infrastructure for Workflows Using Python

Orchestrating the execution of ensembles of processes lies at the core of scientific workflow engines on large scale parallel platforms. This is usually handled using platform-specific command line tools, with limited process management control and potential strain on system resources. The PMIx standard provides a uniform interface to system resources. The low level C implementation of PMIx has hampered its use in workflow engines, leading to the development of Python binding that has yet to gain traction. In this paper, we present our work to harden the PMIx Python client, demonstrating its usability using a prototype Python driver to orchestrate the execution of an ensemble of processes. We present experimental results using the prototype on the Summit supercomputer at Oak Ridge National Laboratory. This work lays the foundation for wider adoption of PMIx for workflow engines, and encourages wider support of more PMIx functionality in vendor provided system software stacks.

Elwasif, Wael↗

The CUORE cryostat: An infrastructure for rare event searches at millikelvin temperatures

The CUORE experiment is the world's largest bolometric experiment. The detector consists of an array of 988 TeO 2 crystals, for a total mass of 742 kg. CUORE is presently taking data at the Laboratori Nazionali del Gran Sasso, Italy, searching for the neutrinoless double beta decay of 130 Te. A large custom cryogen-free cryostat allows reaching and maintaining a base temperature of ~10 mK, required for the optimal operation of the detector. This apparatus has been designed in order to achieve a low noise environment, with minimal contribution to the radioactive background for the experiment. In this paper, we present an overview of the CUORE cryostat, together with a description of all its sub-systems, focusing on the solutions identified to satisfy the stringent requirements. We briefly illustrate the various phases of the cryostat commissioning and highlight the relevant steps and milestones achieved each time. Finally, we describe the successful cooldown of CUORE.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Optimizing fluvial flood mitigation strategies: A multi-objective approach for cost-effective and socially-aware infrastructure feasibility analysis

Effective levee planning must balance capital cost, risk reduction, and community priorities. These objectives are rarely optimized together. This study presents a feasibility phase, simulationin-the-loop framework that couples terrain-based flood modeling with a socially aware multiobjective optimizer. Flood risk is measured as Expected Annual Exposed Population (EAEP), obtained by integrating exposure over Annual Exceedance Probability (AEP) nodes, mirroring the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA) expected-annual formulation but with people rather than dollars. Exposure per scenario is computed by overlaying binary inundation masks with a population surface at the tract level. Distributional fairness is encoded through a Group Benefit Share (GBS) constraint that requires high-SVI tracts to receive at least a baseline share of annualized benefits. Capital cost is represented by a height-dependent unit-cost model suitable for screening. This study addresses the two-objective problem, minimize cost and expected annual exposure subject to the GBS constraint, using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and leveraging Pareto front for feasibility phase decision making. Implemented with terrain-based flood modeling, GeoFlood, for rapid scenario evaluation, the framework is demonstrated in Southeast Texas. The results reveal clear trade-offs among cost, risk, and social benefits and identify non-dominated levee height configurations that satisfy the benefit-share floor. The contributions are a scalable decision support method that operationalizes expected annual population-based risk, embeds enforceable benefit-sharing guarantees, and uses lightweight simulation to explore large design spaces before higher fidelity design stages.

Flood mitigation↗