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: 0833062
• 负责人: Karsten Schwan
• 金额: --
• 依托单位: Georgia Tech Research Corporation
• 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=0833062&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.

大型科学应用程序具有复杂的通信需求，包括机器内点对点和全局通信、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。 用于多学科调查的耦合科学模型进一步增加了这一复杂性。这种多用途的，丰富的，和动态的数据通信的性质，在未来的exascale代码提出了第一个挑战，解决了本research.Further，考虑到未来的计算机芯片的众核性质和可能存在的专门的硬件加速器内核，应用级通信和I/O面临着越来越复杂的一套片上，跨节点，跨机器互连。 在未来的exascale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。 总而言之，未来科学发现的亿级应用程序开发人员面临的问题是如何有效地管理他们的通信需求的复杂性，同时保护他们最关键的“核心”通信免受干扰。这些模型称为C-Model，将描述和实现用于在线分析、存储和可视化目的的I/O以及用于耦合应用程序代码之间的数据移动的通信，并且还将捕获所有上述内容所暗示的数据移动与每个单个应用程序固有的内部数据通信的交互。 这种对通信复杂性的抽象和封装是驯服未来兆兆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，从而有助于最大限度地提高这些应用程序在领导级机器上的性能。