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Titlebook: Supercomputing; 6th Russian Supercom Vladimir Voevodin,Sergey Sobolev Conference proceedings 2020 Springer Nature Switzerland AG 2020 cloud

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發(fā)表于 2025-3-21 16:37:51 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Supercomputing
副標(biāo)題6th Russian Supercom
編輯Vladimir Voevodin,Sergey Sobolev
視頻videohttp://file.papertrans.cn/882/881799/881799.mp4
叢書(shū)名稱Communications in Computer and Information Science
圖書(shū)封面Titlebook: Supercomputing; 6th Russian Supercom Vladimir Voevodin,Sergey Sobolev Conference proceedings 2020 Springer Nature Switzerland AG 2020 cloud
描述.This book constitutes the refereed post-conference proceedings of the 6th Russian Supercomputing Days, RuSCDays 2020, held in Moscow, Russia, in September 2020.*..The 51 revised full and 4 revised short papers presented were carefully reviewed and selected from 106 submissions. The papers are organized in the following topical sections: parallel algorithms; supercomputer simulation; HPC, BigData, AI: architectures, technologies, tools; and distributed and cloud computing...*. The conference was held virtually due to the COVID-19 pandemic..
出版日期Conference proceedings 2020
關(guān)鍵詞cloud computing; computer hardware; computer networks; computer programming; computer systems; distribute
版次1
doihttps://doi.org/10.1007/978-3-030-64616-5
isbn_softcover978-3-030-64615-8
isbn_ebook978-3-030-64616-5Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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書(shū)目名稱Supercomputing影響因子(影響力)學(xué)科排名




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書(shū)目名稱Supercomputing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Supercomputing被引頻次




書(shū)目名稱Supercomputing被引頻次學(xué)科排名




書(shū)目名稱Supercomputing年度引用




書(shū)目名稱Supercomputing年度引用學(xué)科排名




書(shū)目名稱Supercomputing讀者反饋




書(shū)目名稱Supercomputing讀者反饋學(xué)科排名




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Parallel Box-Counting Method for Evaluating the Fractal Dimension of Analytically Defined Curvesgments with a billion boxes (box size is .). The graphs depicting an increase in acceleration of parallel code performance with decreasing the box size and increasing the number of curve segments are shown. It is concluded that the efficiency of using the GPU begins with three million boxes and grow
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alyze the evaluation performance using various metrics such as accuracy, loss, and f1 score. The proposed work recorded 100, 90, and 88.23% accuracy for training, validation, and test sets, respectively. The f1 score for the test set is estimated at 87.78% with a 0.50 logloss score.
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