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.

现在，我们正处于大数据革命之中，在大数据革命中，企业服务越来越受数据分析提供支持的运营和业务模型。使大数据成功的关键部分是确保基本数据处理原始图可以在大量和增加的数据量中有效地执行。但是，如今的数据处理内核主要采用了大约三十年前设计的技术，现在已经与现代硬件脱节，这已经发生了根本的技术转变。首先，在功耗特性的驱动下，现代处理器现在有多个加工单元（称为核心）。相比之下，几年前的处理器是单一核心。其次，传统上，数据存储介质是磁性硬盘。现在，数据已开始几乎永久移动到更高的内存层次结构，更具体地转移到主内存。该项目的目的是重新考虑现代多核和主要内存硬件环境数据处理技术的关键方面。研究方法是设计，实施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，更节能的数据内核来对大数据生态系统产生广泛的影响。