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

该项目基于一种名为张量网络的新兴技术，为新兴和未来的数据分析和挖掘应用提供高效的并行算法和软件。张量是矩阵的高维推广，在信号和图像处理、计算机视觉、医疗保健分析和神经科学等领域都有应用，仅举几例。然而，尽管有这种需求，但还没有针对可能具有许多并行处理器的服务器系统的全面、高性能的软件基础设施。因此，该项目的总体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放源码的包，称为并行TensorInfrastructure，或Parti！党的更广泛的影响！该项目是使张量的使用，在各种数据处理领域，更容易做和更广泛的。该项目将专门关注单节点、多核和多核加速平台上稀疏张量的算法和软件支持。技术方法依赖于一种表示张量的特定方式，称为张量网络。张量网络是表示高阶张量或张量因式分解结构的有效方法。它可以被数据分析员用来作为一种简单、高级的方式来表达他或她在时态所代表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者的工具，它也是一种抽象的中间形式，可以从中推导算法，表达和管理并行性，并半自动生成张量处理软件。这种洞察力与高性能稀疏线性代数中众所周知的数据布局和避免通信的并行化技术相结合，将使部分！用于基于张量的数据分析。该项目将通过评估该部分来展示该方法的实用性！通过与政府研究实验室和行业合作伙伴合作，建立真实数据集和系统的原型。有关更多信息，请参阅该项目的网站：parti-project t.org

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