Graphulo: Linear Algebra Graph Kernels for NoSQL Databases

Vijay Gadepally, Jake Bolewski, Dan Hook, Dylan Hutchison, Benjamin A. Miller, Jeremy Kepner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 29th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages822-830
Number of pages9
ISBN (Electronic)0769555101, 9780769555102
DOIs
StatePublished - 29 Sep 2015
Externally publishedYes
Event29th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015 - Hyderabad, India
Duration: 25 May 201529 May 2015

Publication series

NameProceedings - 2015 IEEE 29th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015

Conference

Conference29th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015
Country/TerritoryIndia
CityHyderabad
Period25/05/1529/05/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Algorithm design and analysis
  • Arrays
  • Databases
  • Kernel
  • Linear algebra
  • Matrix converters
  • Sparse matrices

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture

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