Tall and skinny QR factorizations in MapReduce architectures

Authors:
Paul G. Constantine;David F. Gleich
Affiliations:
Sandia National Laboratories, Albuquerque, CA, USA;Sandia National Laboratories, Livermore, CA, USA
Venue:
Proceedings of the second international workshop on MapReduce and its applications
Year:
2011

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Abstract

The QR factorization is one of the most important and useful matrix factorizations in scientific computing. A recent communication-avoiding version of the QR factorization trades flops for messages and is ideal for MapReduce, where computationally intensive processes operate locally on subsets of the data. We present an implementation of the tall and skinny QR (TSQR) factorization in the MapReduce framework, and we provide computational results for nearly terabyte-sized datasets. These tasks run in just a few minutes under a variety of parameter choices.