CSR: Small: Hardware Architectures for Data Mining at the Exascale

CSR：小型：用于百亿亿级数据挖掘的硬件架构

• 批准号: 1116810
• 负责人: Joseph Zambreno
• 金额: $ 45万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116810&HistoricalAwards=false
• 关键词: CSR; Small; Hardware; Architectures; Data

The algorithms and techniques used to find useful patterns in large sets of data, collectively known as data mining and information visualization, have become vital to researchers making discoveries in diverse fields. The goals of data mining (and computing in general) at the Exascale have introduced fundamental challenges at the architectural level, and even though the evolution of GPU architecture has partly been driven by these challenges, overall computing system performance is not increasing at an equal rate as that of data generation and collection, thus widening the gap between the capabilities of mining algorithms and the performance of real-world data mining systems. This project investigates the design of new hardware/software platforms that will enable existing data mining algorithms to scale with increasingly large and complex datasets. In doing so, this project builds upon current understanding of the characteristics of data mining applications that differ from those for which modern processors are currently designed, similar to what already has been done in the signal processing and network processing domains. This project also studies a variety of design methodologies and models of computation that could lead to performance improvements. Finally, this project analyzes the inherent tradeoffs between algorithmic accuracy and architecture overhead in an attempt to generalize the accuracy and performance tradeoff. The expected impact is that these research tasks will contribute to the growing body of work in embedded system design at the hardware/software interface, and will help to develop a part of future hybrid multi-core computing platforms.

用于在大数据集中发现有用模式的算法和技术，统称为数据挖掘和信息可视化，对于研究人员在不同领域的发现已经变得至关重要。Exascale上的数据挖掘（和一般的计算）的目标在架构层面上引入了基本的挑战，尽管GPU架构的发展部分是由这些挑战驱动的，但整体计算系统的性能并没有以数据生成和收集的相同速度增长，从而扩大了挖掘算法的能力与现实世界数据挖掘系统的性能之间的差距。该项目研究了新的硬件/软件平台的设计，这些平台将使现有的数据挖掘算法能够适应日益庞大和复杂的数据集。在这样做的过程中，本项目建立在当前对数据挖掘应用程序特征的理解之上，这些特征与现代处理器当前设计的特征不同，类似于信号处理和网络处理领域已经完成的工作。该项目还研究了各种可能导致性能改进的设计方法和计算模型。最后，本项目分析了算法精度和架构开销之间的内在权衡，试图推广精度和性能权衡。预期的影响是，这些研究任务将有助于嵌入式系统的硬件/软件接口设计，并将有助于开发未来混合多核计算平台的一部分。