. . "216255" . "4"^^ . . . "Non-Negative Tensor Factorization Accelerated Using GPGPU"@en . "Herout, Adam" . . . . . . . "RIV/00216305:26230/11:PU89632" . . "6"^^ . "Non-Negative Tensor Factorization Accelerated Using GPGPU" . . . . "[36265A78A2A5]" . "This article presents an optimized algorithm for Non-Negative Tensor Factorization (NTF), implemented in the CUDA (Compute Uniform Device Architecture) framework, that runs on contemporary graphics processors and exploits their massive parallelism. The NTF implementation is primarily targeted for analysis of high-dimensional spectral images, including dimensionality reduction, feature extraction, and other tasks related to spectral imaging; however, the algorithm and its implementation are not limited to spectral imaging. The speed-ups measured on real spectral images are around 60-100x compared to a traditional\u00A0 C implementation compiled with an optimizing compiler.\u00A0 Since common problems in the field of spectral imaging may take hours on a state-of-the-art CPU, the speed-up achieved using a graphics card is attractive.\u00A0 The implementation is publicly available in the form of a dynamically linked library, including an interface to MATLAB, and thus may be of help to researchers and eng" . "2011" . "Hauta-Kasari, Markku" . . "RIV/00216305:26230/11:PU89632!RIV13-MSM-26230___" . "US - Spojen\u00E9 st\u00E1ty americk\u00E9" . "Non-Negative Tensor Factorization Accelerated Using GPGPU"@en . "Jo\u0161th, Radovan" . "Non-Negative Tensor Factorization Accelerated Using GPGPU" . . "This article presents an optimized algorithm for Non-Negative Tensor Factorization (NTF), implemented in the CUDA (Compute Uniform Device Architecture) framework, that runs on contemporary graphics processors and exploits their massive parallelism. The NTF implementation is primarily targeted for analysis of high-dimensional spectral images, including dimensionality reduction, feature extraction, and other tasks related to spectral imaging; however, the algorithm and its implementation are not limited to spectral imaging. The speed-ups measured on real spectral images are around 60-100x compared to a traditional\u00A0 C implementation compiled with an optimizing compiler.\u00A0 Since common problems in the field of spectral imaging may take hours on a state-of-the-art CPU, the speed-up achieved using a graphics card is attractive.\u00A0 The implementation is publicly available in the form of a dynamically linked library, including an interface to MATLAB, and thus may be of help to researchers and eng"@en . "7"^^ . . "Antikainen, Jukka" . "Jo\u0161th, Radovan" . "Zem\u010D\u00EDk, Pavel" . . "1111" . . . "1045-9219" . "26230" . "P(LC06008), S, Z(MSM0021630528)" . "IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS" . "Non-negative tensor factorization, spectral analysis, GPU"@en . "Havel, Ji\u0159\u00ED" . .