Graphulo: Linear Algebra Graph Kernels for NoSQL Databases
Graphulo: Linear Algebra Graph Kernels for NoSQL Databases
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Graphulo：NoSQL 数据库的线性代数图内核
DOI：
10.1109/ipdpsw.2015.19
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
J. Kepner
中科院分区：
文献类型：
--
作者：
V. Gadepally;Jake Bolewski;D. Hook;D. Hutchison;B. A. Miller;J. Kepner
大数据和物联网时代继续挑战计算系统。已经开发了几种技术解决方案，例如NOSQL数据库来应对这一挑战。为了从大型数据集中产生有意义的结果，分析师经常使用图表表示，该图表提供了一种直观的方式来与数据合作。图顶点可以表示用户和事件，边缘可以表示顶点之间的关系。图形算法用于从这些非常大的图表中提取有意义的信息。在麻省理工学院，Grusthulo倡议是一种直接在NOSQL数据库中执行图形算法的努力，例如Apache Accumulo或SCIDB，它们具有固有的稀疏数据存储方案。稀疏矩阵操作具有有效实现的历史记录，图基本线性代数子程序（Graph Blas）社区开发了一组关键内核，可用于开发有效的线性代数操作。但是，为了使用图形BLAS内核，重要的是要使用线性代数构建块重铸通用图形算法。在本文中，我们查看了图形算法的常见类别，并使用图形BLAS构建块将其重新铸造为线性代数操作。
Big data and the Internet of Things era continue to challenge computational systems. Several technology solutions such as NoSQL databases have been developed to deal with this challenge. In order to generate meaningful results from large datasets, analysts often use a graph representation which provides an intuitive way to work with the data. Graph vertices can represent users and events, and edges can represent the relationship between vertices. Graph algorithms are used to extract meaningful information from these very large graphs. At MIT, the Graphulo initiative is an effort to perform graph algorithms directly in NoSQL databases such as Apache Accumulo or SciDB, which have an inherently sparse data storage scheme. Sparse matrix operations have a history of efficient implementations and the Graph Basic Linear Algebra Subprogram (Graph BLAS) community has developed a set of key kernels that can be used to develop efficient linear algebra operations. However, in order to use the Graph BLAS kernels, it is important that common graph algorithms be recast using the linear algebra building blocks. In this article, we look at common classes of graph algorithms and recast them into linear algebra operations using the Graph BLAS building blocks.