Collaborative Research: Actively Managing Data Movement with Models - Taming High Performance Data Communications in Exascale Machines

协作研究：通过模型主动管理数据移动 - 驯服百亿亿次机器中的高性能数据通信

• 批准号: 0833039
• 负责人: Yicheng Lu
• 金额: --
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0833039&HistoricalAwards=false
• 关键词: Collaborative; Research; Actively; Managing; Data

Large scientific applications have complex communication needs, including intra-machine point-to-point and global communications, cross-machine checkpointing, and data outputs for online validation and remote data display. Coupled scientific models for multidisciplinary investigations further add to this complexity. This multi-purpose, rich, and dynamic nature of data communications in future exascale codes presents the first challenge addressed by this research. Further, given the many-core nature of future computer chips and the likely presence of specialized hardware accelerator cores, application-level communications and I/O face an increasingly complex set of on-chip, cross-node, and cross-machine interconnects. The complex nature of the physical communication infrastructures present in future exascale machines is the second challenge addressed by this research. In summary, the problem facing developers of future exascale applications for scientific discovery is how to effectively manage the complexity of their communication needs while protecting their most critical `core' communications from perturbation. This project will develop higher level, explicit models for the data communications performed in future exascale codes. These models, called C-Models, will describe and implement the communications performed for I/O for purposes of online analysis, storage, and visualization, and for data movements across coupled application codes, and will also capture the interaction of the data movements implied by all of the above with the internal data communications inherent to each single application. This abstraction and encapsulation of communication complexity is key to taming the complexity of future exascale applications. The C-Model infrastructure will also help protect the critical core communication component of these applications from perturbation, helping to maximize the performance of these applications on leadership-class machines.

大型科学应用具有复杂的通信需求，包括机器内部点对点和全局通信，跨机器检查点，以及用于在线验证和远程数据显示的数据输出。多学科研究的耦合科学模型进一步增加了这种复杂性。未来百亿亿级代码中数据通信的多用途、丰富和动态特性是本研究解决的第一个挑战。此外，考虑到未来计算机芯片的多核特性和可能出现的专用硬件加速器核心，应用程序级通信和I/O将面临越来越复杂的片上、跨节点和跨机器互连。未来百亿亿次机器中存在的物理通信基础设施的复杂性是本研究解决的第二个挑战。总之，未来用于科学发现的百亿亿次应用程序的开发人员面临的问题是如何有效地管理其通信需求的复杂性，同时保护其最关键的“核心”通信不受干扰。该项目将为未来百亿亿级代码中执行的数据通信开发更高级别的显式模型。这些被称为c模型的模型将描述和实现用于在线分析、存储和可视化以及跨耦合应用程序代码的数据移动的I/O执行的通信，并且还将捕获上述所有内容所隐含的数据移动与每个单个应用程序固有的内部数据通信之间的交互。这种对通信复杂性的抽象和封装是控制未来百亿亿级应用程序复杂性的关键。C-Model基础设施还将有助于保护这些应用程序的关键核心通信组件免受干扰，帮助最大限度地提高这些应用程序在领导级机器上的性能。