High-Performance Graph Algorithms from Parallel Sparse Matrices

  • John R. Gilbert
  • Steve Reinhardt
  • Viral B. Shah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4699)


Large-scale computation on graphs and other discrete structures is becoming increasingly important in many applications, including computational biology, web search, and knowledge discovery. High-performance combinatorial computing is an infant field, in sharp contrast with numerical scientific computing.

We argue that many of the tools of high-performance numerical computing – in particular, parallel algorithms and data structures for computation with sparse matrices – can form the nucleus of a robust infrastructure for parallel computing on graphs. We demonstrate this with an implementation of a graph analysis benchmark using the sparse matrix infrastructure in Star-P, our parallel dialect of the Matlab programming language.


Sparse Matrix Input Graph Large Graph Sparse Matrice Basic Design Principle 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • John R. Gilbert
    • 1
  • Steve Reinhardt
    • 2
  • Viral B. Shah
    • 1
  1. 1.University of California, Dept. of Computer Science, Harold Frank Hall, Santa Barbara, CA 93106USA
  2. 2.Silicon Graphics Inc. 

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