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Wyszukujesz frazę "GPU computing" wg kryterium: Wszystkie pola


Tytuł:
G-DNA – a highly efficient multi-GPU/MPI tool for aligning nucleotide reads
Autorzy:
Frohmberg, W.
Kierzynka, M.
Blazewicz, J.
Gawron, P.
Wojciechowski, P.
Powiązania:
https://bibliotekanauki.pl/articles/200827.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
DNA assembly preprocessing
sequence alignment
GPU computing
Opis:
DNA/RNA sequencing has recently become a primary way researchers generate biological data for further analysis. Assembling algorithms are an integral part of this process. However, some of them require pairwise alignment to be applied to a great deal of reads. Although several efficient alignment tools have been released over the past few years, including those taking advantage of GPUs (Graphics Processing Units), none of them directly targets high-throughput sequencing data. As a result, a need arose to create software that could handle such data as effectively as possible. G-DNA (GPU-based DNA aligner) is the first highly parallel solution that has been optimized to process nucleotide reads (DNA/RNA) from modern sequencing machines. Results show that the software reaches up to 89 GCUPS (Giga Cell Updates Per Second) on a single GPU and as a result it is the fastest tool in its class. Moreover, it scales up well on multiple GPUs systems, including MPI-based computational clusters, where its performance is counted in TCUPS (Tera CUPS).
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 4; 989-992
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Heterogeneous GPU&CPU cluster for High Performance Computing in cryptography
Autorzy:
Marks, M.
Jantura, J.
Niewiadomska-Szynkiewicz, E.
Strzelczyk, P.
Góźdź, K.
Powiązania:
https://bibliotekanauki.pl/articles/305288.pdf
Data publikacji:
2012
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
parallel computing
HPC
clusters
GPU computing
OpenCL
cryptography
cryptanalysis
Opis:
This paper addresses issues associated with distributed computing systems and the application of mixed GPU&CPU technology to data encryption and decryption algorithms. We describe a heterogenous cluster HGCC formed by two types of nodes: Intel processor with NVIDIA graphics processing unit and AMD processor with AMD graphics processing unit (formerly ATI), and a novel software framework that hides the heterogeneity of our cluster and provides tools for solving complex scientific and engineering problems. Finally, we present the results of numerical experiments. The considered case study is concerned with parallel implementations of selected cryptanalysis algorithms. The main goal of the paper is to show the wide applicability of the GPU&CPU technology to large scale computation and data processing.
Źródło:
Computer Science; 2012, 13 (2); 63-79
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
G-PAS 2.0 - an improved version of protein alignment tool with an efficient backtracking routine on multiple GPUs
Autorzy:
Frohmberg, W.
Kierzynka, M.
Blazewicz, J.
Wojciechowski, P.
Powiązania:
https://bibliotekanauki.pl/articles/201593.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
pairwise alignment
GPU computing
alignment with backtracking procedure
Opis:
Several highly efficient alignment tools have been released over the past few years, including those taking advantage of GPUs (Graphics Processing Units). G-PAS (GPU-based Pairwise Alignment Software) was one of them, however, with a couple of interesting features that made it unique. Nevertheless, in order to adapt it to a new computational architecture some changes had to be introduced. In this paper we present G-PAS 2.0 - a new version of the software for performing high-throughput alignment. Results show, that the new version is faster nearly by a fourth on the same hardware, reaching over 20 GCUPS (Giga Cell Updates Per Second).
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 491-494
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Novel GPU-Enabled Simulator for Large Scale Spiking Neural Networks
Autorzy:
Szynkiewicz, P.
Powiązania:
https://bibliotekanauki.pl/articles/307680.pdf
Data publikacji:
2016
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
GPU computing
OpenCL programming technology
parallel simulation
spiking neural networks
Opis:
The understanding of the structural and dynamic complexity of neural networks is greatly facilitated by computer simulations. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper a framework for modeling and parallel simulation of biological-inspired large scale spiking neural networks on high-performance graphics processors is described. This tool is implemented in the OpenCL programming technology. It enables simulation study with three models: Integrate-andfire, Hodgkin-Huxley and Izhikevich neuron model. The results of extensive simulations are provided to illustrate the operation and performance of the presented software framework. The particular attention is focused on the computational speed-up factor.
Źródło:
Journal of Telecommunications and Information Technology; 2016, 2; 34-42
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Hybrid CPU/GPU Cluster for Encryption and Decryption of Large Amounts of Data
Autorzy:
Niewiadomska-Szynkiewicz, E.
Marks, M.
Jantura, J.
Podbielski, M.
Powiązania:
https://bibliotekanauki.pl/articles/309363.pdf
Data publikacji:
2012
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
AES
computer clusters
cryptography
DES
GPU computing
parallel calculation
software systems
Opis:
The main advantage of a distributed computing system over standalone computer is an ability to share the workload between cores, processors and computers. In our paper we present a hybrid cluster system - a novel computing architecture with multi-core CPUs working together with many-core GPUs. It integrates two types of CPU, i.e., Intel and AMD processor with advanced graphics processing units, adequately, Nvidia Tesla and AMD FirePro (formerly ATI). Our CPU/GPU cluster is dedicated to perform massive parallel computations which is a common approach in cryptanalysis and cryptography. The efficiency of parallel implementations of selected data encryption and decryption algorithms are presented to illustrate the performance of our system.
Źródło:
Journal of Telecommunications and Information Technology; 2012, 3; 32-39
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of the Lattice Boltzmann Method to the flow past a sphere
Autorzy:
Kajzer, A.
Pozorski, J.
Powiązania:
https://bibliotekanauki.pl/articles/281895.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
bluff-body flow
Lattice Boltzmann Method
Large Eddy Simulation
GPU computing
Opis:
The results of fully resolved simulations and large eddy simulations of bluff-body flows obtained by means of the Lattice Boltzmann Method (LBM) are reported. A selection of Reynolds numbers has been investigated in unsteady laminar and transient flow regimes. Computed drag coefficients of a cube have been compared with the available data for validation purposes. Then, a more detailed analysis of the flow past a sphere is presented, including also the determination of vortex shedding frequency and the resulting Strouhal numbers. Advantages and drawbacks of the chosen geometry implementation technique, so called “staircase geometry”, are discussed. For the quest of maximum computational effi- ciency, all simulations have been carried out with the use of in-house code executed on GPU.
Źródło:
Journal of Theoretical and Applied Mechanics; 2017, 55, 3; 1091-1099
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Very Fast Non-Dominated Sorting
Autorzy:
Smutnicki, C.
Rudy, J.
Żelazny, D.
Powiązania:
https://bibliotekanauki.pl/articles/375948.pdf
Data publikacji:
2014
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
parallel algorithms
Pareto sorting
computational complexity
GPU computing
multiple criteria decision analysis
NSGA-II
Opis:
A new and very efficient parallel algorithm for the Fast Non-dominated Sorting of Pareto fronts is proposed. By decreasing its computational complexity, the application of the proposed method allows us to increase the speedup of the best up to now Fast and Elitist Multi-Objective Genetic Algorithm (NSGA-II) more than two orders of magnitude. Formal proofs of time complexities of basic as well as improved versions of the procedure are presented. The provided experimental results fully confirm theoretical findings.
Źródło:
Decision Making in Manufacturing and Services; 2014, 8, 1-2; 13-23
1896-8325
2300-7087
Pojawia się w:
Decision Making in Manufacturing and Services
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Exploiting multi-core and many-core parallelism for subspace clustering
Autorzy:
Datta, Amitava
Kaur, Amardeep
Lauer, Tobias
Chabbouh, Sami
Powiązania:
https://bibliotekanauki.pl/articles/331126.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
data mining
subspace clustering
multicore processor
many core processor
GPU computing
eksploracja danych
procesor wielordzeniowy
obliczenia GPU
Opis:
Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset, where a subspace is a subset of dimensions of the data. But the exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, which means that parallelization becomes difficult and inefficient. SUBSCALE is a recent subspace clustering algorithm which is scalable with the dimensions and contains independent processing steps which can be exploited through parallelism. In this paper, we aim to leverage the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation shows linear speedup. Moreover, we develop an approach using graphics processing units (GPUs) for fine-grained data parallelism to accelerate the computation further. First tests of the GPU implementation show very promising results.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 1; 81-91
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wyznaczanie równoległości pętli programowych w aplikacjach dedykowanych dla procesorów graficznych
Parallelizing program loops for graphics processing in general purpose computing
Autorzy:
Bielecki, W.
Pałkowski, M.
Powiązania:
https://bibliotekanauki.pl/articles/155271.pdf
Data publikacji:
2011
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
automatyczne zrównoleglanie pętli
fragmenty kodu
GPU
CUDA
OpenCL
obliczenia wysokiej wydajności
loop parallelization
slices
Opis:
Ekstrakcja równoległości w postaci niezależnych fragmentów kodu pozwala wygenerować równoległe pętle programowe w sposób automatyczny. Kod taki umożliwia wykorzystanie mocy obliczeniowej maszyn równoległych, w tym wieloprocesorowych kart graficznych. W niniejszym artykule poddano analizie zastosowanie algorytmów wyznaczania fragmentów kodu dla aplikacji dedykowanych dla procesorów graficznych. Zbadano przyspieszenie i efektywność obliczeń oraz skalowalność wygenerowanego kodu równoległego.
Extracting synchronization-free slices allows automatically generating parallel loops. The code can be executed on multi-processors machines in a reduced period of time. Slicing techniques enable also generating parallel code for graphics processing in general purpose computing. Nowadays, graphic cards support executing multi-threaded applications. GPU systems consist of tens or hundreds of processors. CUDA (an acronym for Compute Unified Device Architecture) is a parallel computing architecture developed by NVIDIA. Graphics processing units (GPUs) are accessible to software developers through variants of industry standard programming languages. Using CUDA, the latest NVIDIA GPUs become accessible for computation like CPUs. The model for GPU computing is to use a CPU and GPU together in a heterogeneous co-processing computing model. The sequential part of the application runs on the CPU and the computationally-intensive part is accelerated by the GPU. From the user's perspective, the application just runs faster because it uses the high-performance of the GPU to boost performance. In this paper slicing algorithms are examined for generating a parallel code for graphic cards are examined. A short example of the code is presented. CUDA statements and technique are explained. Memory cost and transfer data is considered. Speed-up, efficiency and scalability of the code are analyzed.
Źródło:
Pomiary Automatyka Kontrola; 2011, R. 57, nr 8, 8; 963-965
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
GPU-based tuning of quantum-inspired genetic algorithm for a combinatorial optimization problem
Autorzy:
Nowotniak, R.
Kucharski, J.
Powiązania:
https://bibliotekanauki.pl/articles/201268.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
quantum-inspired genetic algorithm
evolutionary computing
meta-optimization
parallel algorithms
GPGPU
Opis:
This paper concerns efficient parameters tuning (meta-optimization) of a state-of-the-art metaheuristic, Quantum-Inspired Genetic Algorithm (QIGA), in a GPU-based massively parallel computing environment (NVidia CUDATMtechnology). A novel approach to parallel implementation of the algorithm has been presented. In a block of threads, each thread transforms a separate quantum individual or different quantum gene; In each block, a separate experiment with different population is conducted. The computations have been distributed to eight GPU devices, and over 400× speedup has been gained in comparison to Intel Core i7 2.93GHz CPU. This approach allows efficient meta-optimization of the algorithm parameters. Two criteria for the meta-optimization of the rotation angles in quantum genes state space have been considered. Performance comparison has been performed on combinatorial optimization (knapsack problem), and it has been presented that the tuned algorithm is superior to Simple Genetic Algorithm and to original QIGA algorithm.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 2; 323-330
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł

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