MRI: Acquisition of Conflux, A Novel Platform for Data-Driven Computational Physics

MRI：收购数据驱动计算物理的新平台 Conflux

• 批准号: 1531752
• 负责人: Karthikeyan Duraisamy
• 金额: $ 242.3万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1531752&HistoricalAwards=false
• 关键词: MRI; Acquisition; Conflux; Novel; Platform

This project develops an instrument, called ConFlux, hosted at the University of Michigan (UM), specifically designed to enable High Performance Computing (HPC) clusters to communicate seamlessly and at interactive speeds with data-intensive operations. The project establishes a hardware and software ecosystem to enable large scale data-driven modeling of multiscale physical systems. ConFlux will produce advances in predictive modeling in several disciplines including turbulent flows, materials physics, cosmology, climate science and cardiovascular flow modeling. A wide range of phenomena exhibit emergent behavior that makes modeling very challenging. In this project, physics-constrained data-driven modeling approaches are pursued to account for the underlying complexity. These techniques require HPC applications (running on external clusters) to interact with large data sets at run time. ConFlux provides low latency communications for in- and out-of-core data, cross-platform storage, as well as high throughput interconnects and massive memory allocations. The file-system and scheduler natively handle extreme-scale machine learning and traditional HPC modules in a tightly integrated workflow---rather than in segregated operations--leading to significantly lower latencies, fewer algorithmic barriers and less data movement. Course material developed from the usage of ConFlux is being integrated into the educational curriculum via several degree and certificate programs offered by two UM institutes dedicated to computational and data sciences. Use of the ConFlux cluster will be extended to research groups outside of UM utilizing a number of Extreme Science and Engineering Discovery Environment (XSEDE) bridging tools and file-systems. Connections established through UM's Office of Outreach and Diversity are being leveraged to extend the use of ConFlux to minority serving institutions and Historically Black Colleges and Universities. Using the programs developed by the Society of Women Engineers at UM, middle and high school students will be engaged in hands-on educational modules in computing, physics and data.

该项目开发了一种名为ConFlux的仪器，托管在密歇根大学（UM），专门设计用于使高性能计算（HPC）集群能够与数据密集型操作进行无缝和交互式速度的通信。 该项目建立了一个硬件和软件生态系统，以实现多尺度物理系统的大规模数据驱动建模。 ConFlux将在多个学科的预测建模方面取得进展，包括湍流、材料物理学、宇宙学、气候科学和心血管流动建模。大量的现象表现出涌现的行为，这使得建模非常具有挑战性。在这个项目中，物理约束的数据驱动的建模方法，追求占潜在的复杂性。这些技术要求HPC应用程序（运行在外部集群上）在运行时与大型数据集进行交互。ConFlux为核内外数据、跨平台存储以及高吞吐量互连和大规模内存分配提供低延迟通信。文件系统和调度程序在紧密集成的工作流中原生地处理极端规模的机器学习和传统HPC模块-而不是在隔离的操作中-从而显着降低延迟，减少算法障碍和数据移动。 使用ConFlux开发的课程材料正在通过两个致力于计算和数据科学的UM研究所提供的几个学位和证书课程整合到教育课程中。ConFlux集群的使用将扩展到UM以外的研究小组，利用一些极端科学和工程发现环境（XSEDE）桥接工具和文件系统。通过UM的外联和多样性办公室建立的联系正在被利用，以扩大使用ConFlux少数民族服务机构和历史上的黑人学院和大学。使用由女工程师协会在UM开发的程序，初中和高中学生将从事在计算，物理和数据动手教育模块。