Fine Granularity Sparse QR Factorization for Multicore Based Systems

  • Alfredo Buttari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7134)


The advent of multicore processors represents a disruptive event in the history of computer science as conventional parallel programming paradigms are proving incapable of fully exploiting their potential for concurrent computations. The need for different or new programming models clearly arises from recent studies which identify fine-granularity and dynamic execution as the keys to achieve high efficiency on multicore systems. This work presents an implementation of the sparse, multifrontal QR factorization capable of achieving high efficiency on multicore systems through using a fine-grained, dataflow parallel programming model.


Multifrontal Sparse QR Least-Squares Multicore 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alfredo Buttari
    • 1
  1. 1.CNRS-IRITToulouseFrance

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