XPS: FULL: DSD: A Parallel Tensor Infrastructure (ParTI!) for Data Analysis

XPS：完整：DSD：用于数据分析的并行张量基础设施 (PartTI!)

• 批准号: 1533768
• 负责人: Richard Vuduc
• 金额: $ 75万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533768&HistoricalAwards=false
• 关键词: XPS; FULL; DSD; Parallel; Tensor

This project concerns efficient parallel algorithms and software foremerging and future data analysis and mining applications, based on anemerging class of techniques known as tensor networks. Tensors, whichare higher-dimensional generalizations of matrices, are findingapplications in signal and image processing, computer vision,healthcare analytics, and neuroscience, to name just a few. Yetdespite this demand, there is no comprehensive, high-performancesoftware infrastructure targeting server systems that may have manyparallel processors. Thus, the overarching research goal of thisproject is to design the first such infrastructure. The resultingprototype will be an open-source package, called the Parallel TensorInfrastructure, or ParTI! The broader impact of the ParTI! project isto make the use of tensors, in a variety of data processing domains,much easier to do and more widespread.The ParTI! project will focus specifically on algorithmic and softwaresupport for sparse tensors on single-node multi- and many-coreaccelerated platforms. The technical approach relies on a specific wayof representing tensors, referred to as tensor networks. A tensornetwork is an efficient approach for representing the structure of ahigh-order tensor or tensor factorization. It can used by the dataanalyst as a simple, high-level way to express the specific structureor relationships he or she seeks in the data that the tensorrepresents. However, a tensor network is not just a tool for theanalyst; it is also an abstract intermediate form, from which it ispossible to derive algorithms, express and manage parallelism, andsemi-automatically generate tensor processing software. This insight,combined with well-known data layout and communication-avoidingparallelization techniques from high-performance sparse linearalgebra, is what will enable a ParTI! for tensor-based dataanalysis. The project will show the utility of this approach byevaluating the ParTI! prototype on real data sets and systems, throughcollaborations with government research laboratory and industrypartners.For further information see the project web site at: parti-project.org

该项目涉及高效的并行算法和软件，用于合并和未来的数据分析和挖掘应用，基于新兴的张量网络技术。张量是矩阵的高维推广，在信号和图像处理、计算机视觉、医疗分析和神经科学等领域都有应用。然而，尽管有这样的需求，还没有全面的，高性能的软件基础设施的目标服务器系统，可能有许多并行处理器。因此，本项目的首要研究目标是设计第一个这样的基础设施。由此产生的原型将是一个开源软件包，称为并行TensorInfrastructure，或PartI！党的更广泛的影响！项目是使张量的使用，在各种数据处理领域，更容易做和更广泛。该项目将特别关注单节点多核和多核加速平台上稀疏张量的算法和软件支持。这种技术方法依赖于一种特定的表示张量的方法，称为张量网络。 张量网络是表示高阶张量或张量分解结构的有效方法。它可以被数据分析师用作一种简单的，高层次的方式来表达他或她在张量表示的数据中寻找的特定结构或关系。然而，张量网络不仅仅是分析师的工具;它也是一种抽象的中间形式，从中可以导出算法，表达和管理并行性，并半自动生成张量处理软件。这种洞察力，结合著名的数据布局和通信避免并行化技术，从高性能稀疏线性代数，是什么将使一个PartI！用于基于张量的数据分析。该项目将通过评估PartI！通过与政府研究实验室和工业伙伴的合作，在真实的数据集和系统上建立原型。欲了解更多信息，请访问项目网站：parti-project.org