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HPF-2 Support for Dynamic Sparse Computations

  • R. Asenjo
  • O. Plata
  • E. L. Zapata
  • J. Touriño
  • R. Doallo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1656)

Abstract

There is a class of sparse matrix computations, such as direct solvers of systems of linear equations, that change the fill-in (nonzero entries) of the coefficient matrix, and involve row and column operations (pivoting). This paper addresses the problem of the parallelization of these sparse computations from the point of view of the parallel language and the compiler. Dynamic data structures for sparse matrix storage are analyzed, permitting to efficiently deal with fill-in and pivoting issues. Any of the data representations considered enforces the handling of indirections for data accesses, pointer referencing and dynamic data creation. All of these elements go beyond current data-parallel compilation technology. We propose a small set of new extensions to HPF-2 to parallelize these codes, supporting part of the new capabilities on a runtime library. This approach has been evaluated on a Cray T3E, implementing, in particular, the sparse LU factorization.

Keywords

Sparse Matrix Sparse Code Storage Scheme Sparse Array Runtime Library 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • R. Asenjo
    • 1
  • O. Plata
    • 1
  • E. L. Zapata
    • 1
  • J. Touriño
    • 2
  • R. Doallo
    • 2
  1. 1.Dept. Computer ArchitectureUniversity of MálagaSpain
  2. 2.Dept. Electronics and SystemsUniversity of La CoruñaSpain

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