Journal article
V. Yadav, A. Michalak
2016
2016
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DOI
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APA
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Yadav, V., & Michalak, A. (2016). Technical Note: Improving the computational efficiency of sparse matrix multiplication in linear atmospheric inverse problems.
Chicago/Turabian
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Yadav, V., and A. Michalak. “Technical Note: Improving the Computational Efficiency of Sparse Matrix Multiplication in Linear Atmospheric Inverse Problems” (2016).
MLA
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Yadav, V., and A. Michalak. Technical Note: Improving the Computational Efficiency of Sparse Matrix Multiplication in Linear Atmospheric Inverse Problems. 2016.
BibTeX Click to copy
@article{v2016a,
title = {Technical Note: Improving the computational efficiency of sparse matrix multiplication in linear atmospheric inverse problems},
year = {2016},
author = {Yadav, V. and Michalak, A.}
}
Abstract
Abstract. Matrix multiplication of two sparse matrices is a fundamental operation in linear Bayesian inverse problems for computing covariance matrices of observations and a posteriori uncertainties. Applications of sparse-sparse matrix multiplication algorithms for specific use-cases in such inverse problems remain unexplored. Here we present a hybrid-parallel sparse-sparse matrix multiplication approach that is more efficient by a third in terms of execution time and operation count relative to standard sparse matrix multiplication algorithms available in most libraries. Two modifications of this hybrid-parallel algorithm are also proposed for the types of operations typical of atmospheric inverse problems, which further reduce the cost of sparse matrix multiplication by yielding only upper triangular and/or dense matrices.