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    Wyświetlanie 1-4 z 4
    Tytuł:
    Akceleracja obliczeń komputerowych za pomocą układów graficznych z wykorzystaniem technologii CUDA
    Computing acceleration based on application of the CUDA technology
    Autorzy:
    Stefanowicz, Ł.
    Wiśniewski, R.
    Wiśniewska, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/155246.pdf
    Data publikacji:
    2011
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    procesor
    obliczenia
    równoległość
    CPU
    GPU
    CUDA
    multimedia
    iteracja
    wielowątkowość
    processor
    computing acceleration
    parallelism
    iteration
    multithreading
    Opis:
    W artykule zaprezentowano możliwość zastosowania układów graficznych celem przyspieszenia obliczeń komputerowych. Przedstawiono technologię oraz architekturę CUDA firmy nVidia, a także podstawowe rozszerzenia względem standardów języka C. W referacie omówiono autorskie algorytmy testowe oraz metodykę badań, które przeprowadzono w celu określenia skuteczności akceleracji obliczeń komputerowych z wykorzystaniem procesorów graficznych GPU w porównaniu do rozwiązań tradycyjnych, opartych o CPU.
    The paper deals with application of the graphic processor units (GPUs) to acceleration of computer operations and computations. The traditional computation methods are based on the Central Processor Unit (CPU), which ought to handle all computer operations and tasks. Such a solution is especially not effective in case of distributed systems where some sub-tasks can be performed in parallel. Many parallel threads can accelerate computing, which results in a shorter execution time. In the paper a new CUDA technology and architecture is shown. The presented idea of CUDA technology bases on application of the GPU processors to compu-tation to achieve better performance in comparison with the traditional methods, where CPUs are used. The GPU processors may perform multi-thread calculation. Therefore, especially in case of tasks where concurrency can be applied, CUDA may highly speed-up the computation process. The effectiveness of CUDA technology was verified experimentally. To perform investigations and experiments, the own test modules were used. The library of benchmarks consists of various algorithms, from simple iteration scripts to video processing methods. The results obtained from calculations performed via CPU and via GPU are compared and discussed.
    Źródło:
    Pomiary Automatyka Kontrola; 2011, R. 57, nr 8, 8; 954-956
    0032-4140
    Pojawia się w:
    Pomiary Automatyka Kontrola
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
    Autorzy:
    Puchala, Dariusz
    Stokfiszewski, Kamil
    Wieloch, Kamil
    Powiązania:
    https://bibliotekanauki.pl/articles/2173537.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    graphics processing unit
    GPU
    execution time prediction model
    discrete wavelet transform
    DWT
    lattice structure
    convolution-based approach
    orthogonal transform
    orthogonal filter banks
    time effectiveness
    prediction accuracy
    procesor graficzny
    model przewidywania czasu wykonania
    dyskretna transformata falkowa
    struktura sieciowa
    podejście oparte na splotach
    przekształcenia ortogonalne
    ortogonalne banki filtrów
    efektywność czasowa
    dokładność przewidywania
    Opis:
    Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; e139393, 1--30
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
    Autorzy:
    Puchala, Dariusz
    Stokfiszewski, Kamil
    Wieloch, Kamil
    Powiązania:
    https://bibliotekanauki.pl/articles/2173635.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    graphics processing unit
    GPU
    execution time prediction model
    discrete wavelet transform
    DWT
    lattice structure
    convolution-based approach
    orthogonal transform
    orthogonal filter banks
    time effectiveness
    prediction accuracy
    procesor graficzny
    model przewidywania czasu wykonania
    dyskretna transformata falkowa
    struktura sieciowa
    podejście oparte na splotach
    przekształcenia ortogonalne
    ortogonalne banki filtrów
    efektywność czasowa
    dokładność przewidywania
    Opis:
    Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; art. no. e139393
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
    Autorzy:
    Puchala, Dariusz
    Stokfiszewski, Kamil
    Wieloch, Kamil
    Powiązania:
    https://bibliotekanauki.pl/articles/2173636.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    graphics processing unit
    GPU
    execution time prediction model
    discrete wavelet transform
    DWT
    lattice structure
    convolution-based approach
    orthogonal transform
    orthogonal filter banks
    time effectiveness
    prediction accuracy
    procesor graficzny
    model przewidywania czasu wykonania
    dyskretna transformata falkowa
    struktura sieciowa
    podejście oparte na splotach
    przekształcenia ortogonalne
    ortogonalne banki filtrów
    efektywność czasowa
    dokładność przewidywania
    Opis:
    Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; art. no. e139393
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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