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    Wyświetlanie 1-8 z 8
    Tytuł:
    Passive Radar Parallel Processing Using General-Purpose Computing on Graphics Processing Units
    Autorzy:
    Szczepankiewicz, K.
    Malanowski, M.
    Szczepankiewicz, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/226475.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    PCL
    passive coherent location
    parallel implementation
    NVIDIA CUDA
    Opis:
    In the paper an implementation of signal processing chain for a passive radar is presented. The passive radar which was developed at the Warsaw University of Technology, uses FM radio and DVB-T television transmitters as ”illuminators of opportunity”. As the computational load associated with passive radar processing is very high, NVIDIA CUDA technology has been employed for effective implementation using parallel processing. The paper contains the description of the algorithms implementation and the performance results analysis.
    Źródło:
    International Journal of Electronics and Telecommunications; 2015, 61, 4; 357-363
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Grammar based multi-frontal solver for isogeometric analysis in 1d
    Autorzy:
    Kuźnik, K.
    Paszyński, M
    Calo, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/305531.pdf
    Data publikacji:
    2013
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    graph grammar
    direct solver
    isogeometric finite element method
    NVIDIA CUDA GPU
    Opis:
    In this paper, we present a multi-frontal direct solver for one-dimensional iso-geometric finite element method. The solver implementation is based on the graph grammar (GG) model. The GG model allows us to express the entire solver algorithm, including generation of frontal matrices, merging, and eliminations as a set of basic undividable tasks called graph grammar productions. Having the solver algorithm expressed as GG productions, we can find the partial order of execution and create a dependency graph, allowing for scheduling of tasks into shared memory parallel machine. We focus on the implementation of the solver with NVIDIA CUDA on the graphic processing unit (GPU). The solver has been tested for linear, quadratic, cubic, and higher-order B-splines, resulting in logarithmic scalability.
    Źródło:
    Computer Science; 2013, 14 (4); 589-613
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Preconditioned Conjugate Gradient Method for Solution of Large Finite Element Problems on CPU and GPU
    Autorzy:
    Fialko, S. Y.
    Zeglen, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/307602.pdf
    Data publikacji:
    2016
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    conjugate gradient
    incomplete Cholesky factorization
    iterative solvers
    NVIDIA CUDA
    preconditioned conjugate gradient
    Opis:
    In this article the preconditioned conjugate gradient (PCG) method, realized on GPU and intended to solution of large finite element problems of structural mechanics, is considered. The mathematical formulation of problem results in solution of linear equation sets with sparse symmetrical positive definite matrices. The authors use incomplete Cholesky factorization by value approach, based on technique of sparse matrices, for creation of efficient preconditioning, which ensures a stable convergence for weakly conditioned problems mentioned above. The research focuses on realization of PCG solver on GPU with using of CUBLAS and CUSPARSE libraries. Taking into account a restricted amount of GPU core memory, the efficiency and reliability of GPU PCG solver are checked and these factors are compared with data obtained with using of CPU version of this solver, working on large amount of RAM. The real-life large problems, taken from SCAD Soft collection, are considered for such a comparison.
    Źródło:
    Journal of Telecommunications and Information Technology; 2016, 2; 26-33
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nvidias Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem
    Autorzy:
    Chlebus, Marcin
    Dyczko, Michał
    Woźniak, Michał
    Powiązania:
    https://bibliotekanauki.pl/articles/1356517.pdf
    Data publikacji:
    2021-01-29
    Wydawca:
    Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
    Tematy:
    machine learning
    nvidia
    stock returns
    technical analysis
    fundamental analysis
    Opis:
    Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.
    Źródło:
    Central European Economic Journal; 2021, 8, 55; 44 - 62
    2543-6821
    Pojawia się w:
    Central European Economic Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Stereoscopic video chroma key processing using NVIDIA CUDA
    Autorzy:
    Sagan, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/106272.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    NVIDIA CUDA
    chroma key processing
    GPU
    CPU
    stereoscopic images
    Opis:
    In this paper, I use the NVIDIA CUDA technology to perform the chroma key algorithm on stereoscopic images. NVIDIA CUDA allows to process parallel algorithms on GPU. Input data are stereoscopic images with the monochromatic background and the destination background image. Output data is the combination of inputs by using the chroma key. I compare the algorithm efficiency between the GPU and CPU execution.
    Źródło:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2013, 13, 1; 81-87
    1732-1360
    2083-3628
    Pojawia się w:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Preliminary Evaluation of Convolutional Neural Network Acoustic Model for Iban Language Using NVIDIA NeMo
    Autorzy:
    Michael, Steve Olsen
    Juan, Sarah Samson
    Mit, Edwin
    Powiązania:
    https://bibliotekanauki.pl/articles/2058507.pdf
    Data publikacji:
    2022
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    acoustic modeling
    automatic speech recognition
    convolutional neural network
    CNN
    under-resourced language
    NVIDIA NeMo
    Opis:
    For the past few years, artificial neural networks (ANNs) have been one of the most common solutions relied upon while developing automated speech recognition (ASR) acoustic models. There are several variants of ANNs, such as deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). A CNN model is widely used as a method for improving image processing performance. In recent years, CNNs have also been utilized in ASR techniques, and this paper investigates the preliminary result of an end-to-end CNN-based ASR using NVIDIA NeMo on the Iban corpus, an under-resourced language. Studies have shown that CNNs have also managed to produce excellent word error (WER) rates for the acoustic model on ASR for speech data. Conversely, results and studies concerned with under-resourced languages remain unsatisfactory. Hence, by using NVIDIA NeMo, a new ASR engine developed by NVIDIA, the viability and the potential of this alternative approach are evaluated in this paper. Two experiments were conducted: the number of resources used in the works of our ASR’s training was manipulated, as was the internal parameter of the engine used, namely the epochs. The results of those experiments are then analyzed and compared with the results shown in existing papers.
    Źródło:
    Journal of Telecommunications and Information Technology; 2022, 1; 43--53
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hypergrammar-based parallel multi-frontal solver for grids with point singularities
    Autorzy:
    Gurgul, P.
    Paszyński, M.
    Paszyńska, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/305343.pdf
    Data publikacji:
    2015
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    hypergraph grammar
    direct solver
    h-adaptive finite element method
    NVIDIA CUDA GPU
    Opis:
    This paper describes the application of hypergraph grammars to drive a linear computational cost solver for grids with point singularities. Such graph grammar productions are the first mathematical formalisms used to describe solver algorithms, and each indicates the smallest atomic task that can be executed in parallel, which is very useful in the case of parallel execution. In particular,the partial order of execution of graph grammar productions can be found, and the sets of independent graph grammar productions can be localized. They can be scheduled set by set into a shared memory parallel machine. The graph-grammar-based solver has been implemented with NVIDIA CUDA for GPU. Graph grammar productions are accompanied by numerical results for a 2D case. We show that our graph-grammar-based solver with a GPU accelerator is, by order of magnitude, faster than the state-of-the-art MUMPS solver.
    Źródło:
    Computer Science; 2015, 16 (1); 75-102
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of GPU in the development of 3D hydrodynamics simulators for oil recovery prediction
    Zastosowanie procesorów graficznych GPU w rozwoju trójwymiarowych symulatorów hydrodynamicznych w planowaniu wtórnego wydobycia ropy naftowej
    Autorzy:
    Beisembetov, I. K.
    Bekibaev, T. T.
    Assilbekov, B. K.
    Zhapbasbayev, U. K.
    Kenzhaliev, B. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/299217.pdf
    Data publikacji:
    2012
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    GPU
    CPU
    NVIDIA
    trójwymiarowe symulatory hydrodynamiczne
    planowanie wtórnego wydobycia ropy
    3D hydrodynamics simulators
    prediction of oil recovery
    Opis:
    In this article computer's graphics card application in prediction of oil recovery using the CUDA architecture is studied. CUDA is architecture of parallel computing made by NVIDIA Company. It allows increasing dramatically the calculating performance due to GPU (graphical processors) usage. Calculations were executed on field models with 3 million grid blocks. Material balance equation approximated with IMPES method. As the result of numerical modeling of oil recovery prediction with GPU, dozens of times acceleration of calculations comparing with CPU has been taken.
    Artykuł przedstawia badania nad programem graficznym wykorzystywanym w planowaniu wtórnego wydobycia ropy naftowej z wykorzystaniem równoległego systemu obliczeniowego CUDA. CUDA jest systemem stworzonym przez firmę NVIDIA. Pozwala on na ogromne zwiększenie mocy obliczeniowej poprzez zastosowanie procesorów graficznych GPU. Porównane zostały wyniki osiągnięte od roku 2003 obliczone z wykorzystaniem zwykłego procesora CPU oraz procesora graficznego GPU. Obliczenia zostały wykonane na modelu złożowym wykonanym na siatce przestrzennej złożonej z 3 milionów komórek. Równanie bilansu masowego w przybliżeniu opisuje metoda przepływu dwufazowego w ośrodku porowatym typu IMPES. W rezultacie modelowania numerycznego wtórnego wydobycia ropy naftowej z wykorzystaniem procesora graficznego GPU, wyniki obliczeń uzyskano wielokrotnie szybciej niż w przypadku stosowania procesora typu CPU.
    Źródło:
    AGH Drilling, Oil, Gas; 2012, 29, 1; 75-88
    2299-4157
    2300-7052
    Pojawia się w:
    AGH Drilling, Oil, Gas
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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