## 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 language | English |
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Title of host publication | Proceedings - 2015 IEEE 29th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 822-830 |

Number of pages | 9 |

ISBN (Electronic) | 0769555101, 9780769555102 |

DOIs | |

State | Published - 29 Sep 2015 |

Externally published | Yes |

Event | 29th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015 - Hyderabad, India Duration: 25 May 2015 → 29 May 2015 |

### Publication series

Name | Proceedings - 2015 IEEE 29th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015 |
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### Conference

Conference | 29th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015 |
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Country/Territory | India |

City | Hyderabad |

Period | 25/05/15 → 29/05/15 |

### 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