Scalable Complex Graph Analysis with the Knowledge Discovery Toolbox

Abstract

The Knowledge Discovery Toolbox (KDT) enables domain experts to perform complex analyses of huge datasets on supercomputers using a high-level language without grappling with the difficulties of writing parallel code, calling parallel libraries, or becoming a graph expert. KDT delivers competitive performance from a general-purpose, reusable library for graphs on the order of 10 billion edges and greater. We describe our approach for supporting arbirary vertex and edge attributes, in-place graph filtering, and graph traversal using pre-defined access patterns.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2012
Accession Number
ADA576764

Entities

People

  • Adam Lugowski
  • Aydun Buluc
  • John R. Gilbert
  • Steve Reinhardt

Organizations

  • University of California, Santa Barbara

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algebra
  • Algorithms
  • Biological Sciences
  • Computations
  • Filters
  • Filtration
  • High Performance Computing
  • Indexes
  • Language
  • Linear Algebra
  • Mobile Phones
  • National Security
  • Scalability
  • Signal Processing
  • Sparse Matrix
  • Text Messaging

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Graph Algorithms and Convex Optimization.
  • Systems Analysis and Design