相关实验视频
Updated: Jun 24, 2025

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Generation and Coherent Control of Pulsed Quantum Frequency Combs
Published on: June 8, 2018
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流量操作员的流量操作员
Vanessa Sochat1, Aldo Culquicondor2, Antonio Ojea2
1Lawrence Livermore National Laboratory, Livermore, California, 94550, USA.
F1000Research
|June 13, 2024
概括
融合计算整合了高性能计算 (HPC) 和云原生环境. 在Kubernetes中部署的Flux Operator可实现高效的HPC工作负载管理,提高可扩展性和性能.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
- 高性能计算 高性能计算
背景情况:
- 融合计算融合了高性能计算 (HPC) 和云原生模式.
- 由云经济和需要便携式,灵活的工作流驱动.
- 需要HPC和云社区之间的协作开发.
研究的目的:
- 开发用于融合工作负载管理器的组件.
- 在Kubernetes (云原生) 中集成流程框架 (HPC).
- 实现可扩展的,层次化的资源管理,而不会加重Kubernetes调度器的负担.
主要方法:
- 在Kubernetes中使用流程运算符实现流程.
- 在HPC和云环境之间映射组件和设计决策.
- 进行了比较Flux Operator和MPI Operator性能的实验.
主要成果:
- 流程运营商在Kubernetes中提供按需的HPC工作负载管理.
- 证明了对层次安排的成功整合.
- 实验结果显示,Flux和MPI运营商之间的应用性能相似.
结论:
- 流量运营商通过在Kubernetes中实现高效的HPC工作负载管理来促进融合计算.
- 强调了改善技术创新和合作的潜力.
- 解决了挑战,并概述了融合计算的未来愿景.
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