nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware

Sven Koitka; Christoph M. Friedrich

doi:10.32614/RJ-2016-053

nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware

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.

CRAN packages used

NMF, NMFN, nmfgpu4R, Matrix, SparseM

CRAN Task Views implied by cited packages

Econometrics, Multivariate, NumericalMathematics

Reuse

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Citation

For attribution, please cite this work as

Koitka & Friedrich, "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 = {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}
}