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Scalable Parallel Algorithms for Solving Sparse Systems of Linear Equations  (Make Corrections)  
Anshul Gupta



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Abstract: We have developed a highly parallel sparse Cholesky factorization algorithm that substantially improves the state of the art in parallel direct solution of sparse linear systems---both in terms of scalability and overall performance. It is a well known fact that dense matrix factorization scales well and can be implemented efficiently on parallel computers. However, it had been a challenge to developing efficient and scalable parallel formulations of sparse matrix factorization. Our new... (Update)

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BibTeX entry:   (Update)

@misc{ gupta-scalable,
  author = "Anshul Gupta",
  title = "Scalable Parallel Algorithms for Solving Sparse Systems of Linear Equations",
  url = "citeseer.ist.psu.edu/345931.html" }
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51   Sparse Cholesky factorization on a local memory multiprocess.. (context) - George, Heath et al. - 1988
45   An efficient block-oriented approach to parallel sparse Chol.. (context) - Rothberg, Gupta - 1992
44   Analyzing scalability of parallel algorithms and architectur.. - Kumar, Gupta - 1994
43   Massively parallel methods for engineering and science probl.. - Camp, Plimpton et al. - 1994
41   Task scheduling for parallel sparse Cholesky factorization (context) - Geist, Ng - 1989
41   Parallel solution of triangular systems on distributed-memor.. (context) - Heath, Romine - 1988
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24   Highly parallel sparse Cholesky factorization - Gilbert, Schreiber - 1992
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23   Performance properties of large scale parallel systems - Gupta, Kumar - 1993
21   Parallel formulation of interior point algorithms - Karypis, Gupta et al. - 1994
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