Non-negative Matrix Factorization on GPU

Platoš, Jan; Gajdoš, Petr; Krömer, Pavel; Snåšel, Václav

doi:10.1007/978-3-642-14292-5_4

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…It is worth to mention that our tests do not to include a performance comparison between NMF-mGPU and the other NMF implementations on GPU [ 16 , 40 - 42 ] described in the Background section. As previously stated, these applications do not take into account the available GPU memory, nor make use of multiple GPU devices.…”

Section: Resultsmentioning

confidence: 99%

“…To the best of our knowledge, there are only a few GPU implementations of the NMF algorithm [ 16 , 40 - 42 ], but these domain-specific applications do not perform any blockwise processing since they do not consider the available amount of GPU memory, nor make use of multiple GPU devices. Therefore, they are not suitable for the analysis of current large biological datasets.…”

Section: Introductionmentioning

confidence: 99%

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NMF-mGPU: non-negative matrix factorization on multi-GPU systems

et al. 2015

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BackgroundIn the last few years, the Non-negative Matrix Factorization ( NMF ) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster.In this paper, we present NMF-mGPU, an efficient and easy-to-use implementation of the NMF algorithm that takes advantage of the high computing performance delivered by Graphics-Processing Units ( GPUs ). Driven by the ever-growing demands from the video-games industry, graphics cards usually provided in PCs and laptops have evolved from simple graphics-drawing platforms into high-performance programmable systems that can be used as coprocessors for linear-algebra operations. However, these devices may have a limited amount of on-board memory, which is not considered by other NMF implementations on GPU.Results NMF-mGPU is based on CUDA ( Compute Unified Device Architecture ), the NVIDIA’s framework for GPU computing. On devices with low memory available, large input matrices are blockwise transferred from the system’s main memory to the GPU’s memory, and processed accordingly. In addition, NMF-mGPU has been explicitly optimized for the different CUDA architectures. Finally, platforms with multiple GPUs can be synchronized through MPI ( Message Passing Interface ). In a four-GPU system, this implementation is about 120 times faster than a single conventional processor, and more than four times faster than a single GPU device (i.e., a super-linear speedup).ConclusionsApplications of GPUs in Bioinformatics are getting more and more attention due to their outstanding performance when compared to traditional processors. In addition, their relatively low price represents a highly cost-effective alternative to conventional clusters. In life sciences, this results in an excellent opportunity to facilitate the daily work of bioinformaticians that are trying to extract biological meaning out of hundreds of gigabytes of experimental information. NMF-mGPU can be used “out of the box” by researchers with little or no expertise in GPU programming in a variety of platforms, such as PCs, laptops, or high-end GPU clusters. NMF-mGPU is freely available at https://github.com/bioinfo-cnb/bionmf-gpu.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NMF-mGPU: non-negative matrix factorization on multi-GPU systems

et al. 2015

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“…Finally, the NMF updates could also be ported to the GPU, which has been shown to provide a speed-up of approx. 15× [Platos et al 2010]. Other types of nonnegative factorization algorithms with a similar data size have been demonstrated to run in real time [Wetzstein et al 2012;Heide et al 2014].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Adaptive color display via perceptually-driven factored spectral projection

et al. 2015

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Fundamental display characteristics are constantly being improved, especially resolution, dynamic range, and color reproduction. However, whereas high resolution and high-dynamic range displays have matured as a technology, it remains largely unclear how to extend the color gamut of a display without either sacrificing light throughput or making other tradeoffs. In this paper, we advocate for adaptive color display; with hardware implementations that allow for color primaries to be dynamically chosen, an optimal gamut and corresponding pixel states can be computed in a content-adaptive and user-centric manner. We build a flexible gamut projector and develop a perceptually-driven optimization framework that robustly factors a wide color gamut target image into a set of time-multiplexed primaries and corresponding pixel values. We demonstrate that adaptive primary selection has many benefits over fixed gamut selection and show that our algorithm for joint primary selection and gamut mapping performs better than existing methods. Finally, we evaluate the proposed computational display system extensively in simulation and, via photographs and user experiments, with a prototype adaptive color projector.

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