一个可扩展的系统架构,以解决与耦合计算和数据密集型应用程序的下一代预测仿真工作流

M. Seager
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引用次数: 0

摘要

新兴数字经济的趋势正在推动产品和服务的虚拟表现。创建这些数字孪生需要结合实时数据摄取,现实世界条件下物理产品的模拟,服务交付优化和数据分析,以及ML/DL异常检测和决策。模拟中的不确定性量化也将是一个计算和数据密集型工作流程,将推动模拟改进周期。未来的高端计算系统设计需要理解这些类型的复杂工作流程,并提供一个灵活的框架来优化动态负载条件下的设计和操作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Scalable System Architecture to Addressing the Next Generation of Predictive Simulation Workflows with Coupled Compute and Data Intensive Applications
Trends in the emerging digital economy are pushing the virtual representation of products and services. Creating these digital twins requires a combination of real time data ingestion, simulation of physical products under real world conditions, service delivery optimization and data analytics as well as ML/DL anomaly detection and decision making. Quantification of Uncertainty in the simulations will also be a compute and data intensive workflow that will drive the simulation improvement cycle. Future high-end computing systems designs need to comprehend these types of complex workflows and provide a flexible framework for optimizing the design and operations under dynamic load conditions for them.
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