BIGDATA: Small: DCM: Data Management for Analytics Applications on Modern Architecture

BIGDATA：小型：DCM：现代架构上分析应用程序的数据管理

• 批准号: 1250886
• 负责人: Jignesh Patel
• 金额: $ 68.09万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1250886&HistoricalAwards=false
• 关键词: BIGDATA; Small; DCM; Data; Management

We are now in the midst of the big data revolution where enterprise services are increasingly being driven by operational and business models that are powered by data analysis. A key part in making big data successful is ensuring that basic data processing primitives can execute efficiently on large and every increasing volumes of data. However, data processing kernels today largely employ techniques that have been designed about three decades ago, and are now out of touch with modern hardware that has made a fundamental technological shift. First, driven by power consumption characteristics, modern processors now have multiple processing units (called cores) fabricated in a single chip. In contrast, processors just a few years ago were single core. Second, traditionally the storage media for data has been the magnetic hard disk. Now, data has started to move nearly permanently to higher levels of the memory hierarchy, and more specifically to main memory. The goal of this project is to rethink key aspects of data processing techniques for the modern many-core and main memory hardware environment. The research approach is to design, implement and evaluate various methods for data kernels that can be used to store and process data efficiently. In other words, the key focus is on producing data kernels that "run at the speed of modern hardware." Thus, this project aims to have a broad impact on the big data ecosystem by developing faster, cheaper and more energy-efficient data kernels.

我们现在正处于大数据革命之中，企业服务越来越多地受到数据分析支持的运营和业务模型的驱动。大数据成功的一个关键部分是确保基本数据处理原语可以在大量且不断增加的数据上高效执行。然而，今天的数据处理内核主要采用大约三十年前设计的技术，现在与已经发生根本性技术转变的现代硬件脱节。首先，受功耗特性的驱动，现代处理器现在在单个芯片中制造了多个处理单元（称为核心）。相比之下，几年前的处理器还是单核的。其次，传统上数据的存储介质是磁硬盘。现在，数据已经开始几乎永久地移动到内存层次结构的更高级别，更具体地说是移动到主内存。该项目的目标是重新思考现代多核和主内存硬件环境的数据处理技术的关键方面。研究方法是设计、实现和评估可用于有效存储和处理数据的数据内核的各种方法。换句话说，重点是生产“以现代硬件的速度运行”的数据内核。因此，该项目旨在通过开发更快、更便宜、更节能的数据内核，对大数据生态系统产生广泛影响。