DC: Small: Adaptive Sparse Data Mining On Multicores

pi正在开发和评估数据驱动的三阶段自适应，稀疏多核数据挖掘框架，用于可扩展和有效的监督分类和统计分析。第一阶段试图根据稀疏性、图论结构和数据转换的几何和数字度量来描述数据属性，重点是降维。目标是探索解决方案的质量（分类的准确性和精度）和总体工作（顺序计算成本）之间的权衡，以实现更快的改进方法。第二阶段对转换后的数据进行操作，以增加细粒度到粗粒度的并发程度，同时重构数据以增强重用和访问的局部性。这一阶段提供了计算的加权注释图模型，表明依赖关系、数据共享度量和计算成本。第三阶段利用这个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的线程到核映射，这些映射同时考虑了性能和功耗。因此，pi寻求利用数据集属性的适应性，包括稀疏结构中潜在的计算的近似和并发性，以提高当前和未来具有更大核数、复杂缓存层次结构和片外带宽限制的多核处理器和内存硬件的利用率。

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.