发表机构
Enrico Fermi Institute University of Chicago; Argonne Leadership Computing Facility Argonne National Laboratory（芝加哥大学恩里科·费米研究所; 阿贡国家实验室阿贡领导计算设施）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对百亿亿次平台上大规模实验工作流，提出多阶段任务调度方法，并以SBND蒙特卡洛流水线为例，提供建模工具以优化资源利用。
AI 中文摘要
当前及下一代大规模实验，特别是在高能物理和宇宙学领域，涉及的数据量日益增长，需要百亿亿次计算能力来进行分析和建模。百亿亿次机器给工作流扩展和高效资源利用带来了挑战。我们提出了一种多阶段任务调度方法，用于优化这些工作流的资源利用。我们以短基线近探测器（SBND）的蒙特卡洛仿真流水线为例，演示了任务调度，并提出了用于对这些工作流中的任务调度进行建模的理论和数值工具，这些工具可用于识别最优调度策略，并为给定问题规模选择资源。
英文摘要
Current and next-generation large-scale experiments, especially in high-energy physics and cosmology, involve increasing volumes of data that require exascale computing power to analyze and model. Exascale machines present challenges for workflow scaling and efficient resource usage. We propose a multi-stage task scheduling approach for optimizing resource usage for these workflows. We demonstrate task scheduling for an example Monte Carlo simulation pipeline from the Short-Baseline Near Detector (SBND), and present theoretical and numerical tools for modeling task scheduling in these workflows that can be used to identify optimal scheduling strategies and choose resources for a given problem size.
CommentsAccepted XLOOP 2026 submission. 8 pages, 7 figures
