II-NEW: GEARS - An Infrastructure for Energy-Efficient Big Data Research on Heterogeneous and Dynamic Data

II-新：GEARS - 异构动态数据节能大数据研究的基础设施

• 批准号: 1629888
• 负责人: Ming Zhao
• 金额: $ 75万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629888&HistoricalAwards=false
• 关键词: II; NEW; GEARS; Infrastructure; Energy

Big data technologies have been successfully applied to many disciplines for knowledge discovery and decision making, but the further growth and adoption of the big data paradigm face several critical challenges. First, it is challenging to meet the performance needs of modern big data problems which are inherently more difficult, e.g., learning of heterogeneous and imprecise data, and have more stringent performance requirements, e.g., real-time analysis of dynamic data. Second, power consumption is becoming a serious limiting factor to the further scaling of big data systems and the applications that it can support. These challenges demand a new type of big data systems that incorporate unconventional hardware capable of accelerating data processing and accesses while lowering the system's power consumption. Therefore, this project is developing the needed computational infrastructure to support GEARS (an enerGy-Efficient big-datA Research System) for studying heterogeneous and dynamic data using heterogeneous computing and storage resources. GEARS is a one-of-kind, energy-efficient big-data research infrastructure based on cohesively co-designed software and hardware components. It enables a variety of important studies on heterogeneous and dynamic data and advances the scientific knowledge in computer science as well as other data-driven disciplines. It enhances the training of a large body of undergraduate and graduate students, including many from underrepresented groups, by supporting unique research and education activities. Finally, it also benefits the society by contributing new open-source solutions and with potential commercial applications in support of heterogeneous and dynamic data analysis. The hardware of GEARS includes a cluster of data nodes equipped with heterogeneous processors and storage devices and fine-grained power management capability. The software is developed upon widely-used big data frameworks to support unified programming across CPUs, GPUs, and FPGAs and transparent data access across a deep storage hierarchy integrating DRAM, NVM, SSD, and HDD. GEARS also enables novel systems and algorithms research on learning heterogeneous and dynamic data, including (1) new algorithm partitioning and scheduling schemes for using heterogeneous accelerators and optimizing the performance and energy efficiency of big data tasks; (2) new I/O scheduling and data staging strategies for performance and energy efficiency of the deep big-data storage hierarchy; (3) multi-phase, out-of-core decomposition techniques for large-scale tensors; (4) real-time visual analytics system that links streaming media with simulations for anticipatory analytics; (5) multi-modal deep learning methods with heterogeneous social data; (6) new computational tools for real-time analysis of social unrest using social media; (7) scalable, adaptive, and interactive team detection and assemble system for designing high-performing teams using big network data; (8) rare category analysis and heterogeneous learning algorithms for fast and accurate rare event discoveries with large and heterogeneous social data; and (9) new distributed machine learning framework for learning semantic knowledge from Web-scale images/videos with incomplete/noisy textual annotations. All project results will be shared with the broader community via the project website (http://gears.asu.edu). Publications will be listed on the website with links to their publishers. Data and software downloads will listed on the website with instructions on how to use them. Source code will be hosted on GitHub and a direct link to the repository will also be listed on the project website.

大数据技术已成功应用于许多学科的知识发现和决策，但大数据范式的进一步发展和采用面临着几个关键挑战。首先，满足现代大数据问题的性能需求具有挑战性，这些问题本质上更加困难，例如，学习异构和不精确的数据，并具有更严格的性能要求，例如，动态数据的实时分析。其次，功耗正在成为大数据系统及其支持的应用程序进一步扩展的严重限制因素。这些挑战需要一种新型的大数据系统，该系统包含能够加速数据处理和访问的非常规硬件，同时降低系统的功耗。因此，该项目正在开发所需的计算基础设施，以支持GEARS（一个节能的大数据研究系统），用于使用异构计算和存储资源研究异构和动态数据。GEARS是一种基于紧密协同设计的软件和硬件组件的节能大数据研究基础设施。它可以对异构和动态数据进行各种重要的研究，并推进计算机科学和其他数据驱动学科的科学知识。它通过支持独特的研究和教育活动，加强了对大批本科生和研究生的培训，其中包括许多来自代表性不足群体的学生。最后，它还通过贡献新的开源解决方案和支持异构和动态数据分析的潜在商业应用程序来造福社会。 GEARS的硬件包括一个数据节点集群，配备了异构处理器和存储设备以及细粒度的电源管理功能。该软件基于广泛使用的大数据框架开发，支持跨CPU、GPU和FPGA的统一编程，以及跨集成DRAM、NVM、SSD和HDD的深层存储层次结构的透明数据访问。GEARS还支持学习异构和动态数据的新系统和算法研究，包括（1）使用异构加速器和优化大数据任务的性能和能效的新算法分区和调度方案;（2）用于深度大数据存储层次结构的性能和能效的新I/O调度和数据分级策略;（3）大规模张量的多阶段、核外分解技术;（4）将流媒体与模拟相联系以进行预期分析的实时可视化分析系统;（5）具有异构社交数据的多模态深度学习方法;（6）使用社交媒体实时分析社会动荡的新计算工具;（7）可扩展、自适应和交互式团队检测和组装系统，用于使用大网络数据设计高性能团队;（8）稀有类别分析和异构学习算法，用于快速准确地发现具有大规模和异构社交数据的稀有事件;以及（9）新的分布式机器学习框架，用于从具有不完整/嘈杂文本注释的Web规模图像/视频中学习语义知识。所有项目成果都将通过项目网站（http：//www.example.com）与更广泛的社区分享。gears.asu.edu出版物将在网站上列出，并附有与出版商的链接。数据和软件下载将在网站上列出，并说明如何使用它们。源代码将托管在GitHub上，项目网站上也将列出指向存储库的直接链接。