DC: Small: Adaptive Sparse Data Mining On Multicores

DC：小型：多核上的自适应稀疏数据挖掘

• 批准号: 1017882
• 负责人: Padma Raghavan
• 金额: $ 45万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017882&HistoricalAwards=false
• 关键词: DC; Small; Adaptive; Sparse; Data

The PIs are working on developing and evaluating a data-driven three-phase adaptive, sparse multicore data mining framework for scalable and efficient supervised classification and statistical analysis.Phase-I seeks to characterize data attributes in terms of sparsity, graph-theoretic structure and geometric and numeric measures toward data transformations with a focus on dimensionality reduction. The goal is to explore the trade-offs between quality of solution (accuracy and precision of classification) and total work (sequential computational costs) toward faster, yet improved methods. Phase-II operates on the transformed data to increase the degree of fine to coarse grained concurrency while restructuring the data for enhanced reuse and locality of access. This phase provides a weighted annotated graph model of the computations indicating dependencies, data sharing measures and computational costs.Phase-III utilizes this model to formulate and explore architecture-aware mappings of data mining computations to the multicore processors, including cache and bandwidth aware thread-to-core mappings that consider both performance and power. The PIs thus seek adaptations to utilize data set attributes, including approximations and concurrency of computations latent in the sparsity structure, toward improved utilization of processor and memory hardware on current and future multicores with larger core counts, complex cache hierarchies and off-chip bandwidth constraints.

PI 正在致力于开发和评估数据驱动的三相自适应、稀疏多核数据挖掘框架，以实现可扩展且高效的监督分类和统计分析。第一阶段旨在从稀疏性、图论结构和统计角度来表征数据属性。针对数据转换的几何和数值测量，重点是降维。目标是探索解决方案质量（分类的准确性和精度）和总工作量（连续计算成本）之间的权衡，以实现更快、更改进的方法。第二阶段对转换后的数据进行操作，以提高细粒度到粗粒度的并发程度，同时重组数据以增强重用和访问局部性。此阶段提供计算的加权注释图模型，指示依赖性、数据共享措施和计算成本。第三阶段利用此模型来制定和探索数据挖掘计算到多核处理器的体系结构感知映射，包括缓存和带宽感知线程同时考虑性能和功耗的到核心的映射。因此，PI 寻求调整以利用数据集属性，包括稀疏结构中潜在计算的近似和并发性，以提高当前和未来多核上处理器和内存硬件的利用率，这些多核具有更大的核心数量、复杂的缓存层次结构和片外带宽限制。