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[Paper] Tall and skinny QR factorizations in MapReduce architectures

posted Mar 1, 2012, 9:30 AM by Teng-Yok Lee
Paul G. Constantine and David F. Gleich. 2011. Tall and skinny QR factorizations in MapReduce architectures. In Proceedings of the second international workshop on MapReduce and its applications (MapReduce '11). ACM, New York, NY, USA, 43-50. DOI=10.1145/1996092.1996103 http://doi.acm.org/10.1145/1996092.1996103

http://www.cs.purdue.edu/homes/dgleich/publications/Constantine%202011%20-%20TSQR.pdf

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.

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