RUNMON-RIFT: Adaptive Configuration and Healing for Large-Scale Parameter Inference

Autor:	Udall, Rhiannon, Brandt, Joshua, Manchanda, Grihith, Arulanandan, Adhav, Clark, James, Lange, Jacob, O'Shaughnessy, Richard, Cadonati, Laura
Rok vydání:	2021
Předmět:	General Relativity and Quantum Cosmology Astrophysics - Instrumentation and Methods for Astrophysics
Druh dokumentu:	Working Paper
Popis:	Gravitational wave parameter inference pipelines operate on data containing unknown sources on distributed hardware with unreliable performance. For one specific analysis pipeline (RIFT), we have developed a flexible tool (RUNMON-RIFT) to mitigate the most common challenges introduced by these two uncertainties. On the one hand, RUNMON provides several mechanisms to identify and redress unreliable computing environments. On the other hand, RUNMON provides mechanisms to adjust pipeline-specific run settings, including prior ranges, to ensure the analysis completes and encompasses the physical source parameters. We demonstrate both general features with two controlled examples. Comment: 9 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2110.10243 Zobrazit plný text záznamu View this record from Arxiv