In this work, a novel package called nmfgpu4R is presented, which offers the computation of Non-negative Matrix Factorization (NMF) on Compute Unified Device Architecture (CUDA) platforms within the R environment. Benchmarks show a remarkable speed-up in terms of time per iteration by utilizing the parallelization capabilities of modern graphics cards. Therefore the application of NMF gets more attractive for real-world sized problems because the time to compute a factorization is reduced by an order of magnitude.
NMF, NMFN, nmfgpu4R, Matrix, SparseM
Econometrics, Multivariate, NumericalMathematics
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For attribution, please cite this work as
Koitka & Friedrich, "The R Journal: nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware", The R Journal, 2016
BibTeX citation
@article{RJ-2016-053,
author = {Koitka, Sven and Friedrich, Christoph M.},
title = {The R Journal: nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-053},
doi = {10.32614/RJ-2016-053},
volume = {8},
issue = {2},
issn = {2073-4859},
pages = {382-392}
}