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Titlebook: High Performance Computing; 7th Latin American C Sergio Nesmachnow,Harold Castro,Andrei Tchernykh Conference proceedings 2021 The Editor(s)

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發(fā)表于 2025-3-21 18:51:10 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)High Performance Computing
副標(biāo)題7th Latin American C
編輯Sergio Nesmachnow,Harold Castro,Andrei Tchernykh
視頻videohttp://file.papertrans.cn/427/426306/426306.mp4
叢書(shū)名稱(chēng)Communications in Computer and Information Science
圖書(shū)封面Titlebook: High Performance Computing; 7th Latin American C Sergio Nesmachnow,Harold Castro,Andrei Tchernykh Conference proceedings 2021 The Editor(s)
描述This book constitutes revised selected papers of the 7th?Latin American High Performance Computing Conference,?CARLA 2020, held in Cuenca, Ecuador, in September 2020. Due to the COVID-19 pandemic the conference was held in a virtual mode.?.The 15 revised full papers presented were carefully reviewed and selected out of 36 submissions. The papers included in this book are organized according to the topics on ?High Performance Computing Applications;?High Performance Computing and Artificial Intelligence..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; cloud computing; computer hardware; computer networks; computer systems; computi
版次1
doihttps://doi.org/10.1007/978-3-030-68035-0
isbn_softcover978-3-030-68034-3
isbn_ebook978-3-030-68035-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書(shū)目名稱(chēng)High Performance Computing影響因子(影響力)




書(shū)目名稱(chēng)High Performance Computing影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)High Performance Computing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)High Performance Computing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)High Performance Computing被引頻次




書(shū)目名稱(chēng)High Performance Computing被引頻次學(xué)科排名




書(shū)目名稱(chēng)High Performance Computing年度引用




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書(shū)目名稱(chēng)High Performance Computing讀者反饋




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Fostering Remote Visualization: Experiences in Two Different HPC Sitestion is of crucial importance to access infrastructure, data and computational resources and, to avoid data movement from where data is produced and to where data will be analyzed. Remote visualization enables geographically diverse collaboration and enhances user experience through graphical user i
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Accelerating Machine Learning Algorithms with TensorFlow Using Thread Mapping Policiesthms as an important concern. In this work, we explore mappings of threads in multi-core architectures and their impact on new ML algorithms running with Python and TensorFlow. Using smart thread mapping, we were able to reduce the execution time of both training and inference phases for up?to 46% a
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Methodology for Design and Implementation an Efficient HPC Clusters that make up the infrastructure services. Each administrator based on their experience and knowledge assumes a series of considerations to design and implement a cluster that is considered efficient by installing base tools such as NTP, NFS, a task manager (that is, SLURM), LDAP, among others. In
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Estimating the Execution Time of the Coupled Stage in Multiscale Numerical Simulationsr users. The goal of the present work is to estimate the execution time of simulation applications driven by multiscale numerical methods. In computational terms, these methods induce a two-stage simulation process. Fundamentally, the number of possibilities for configuring this two-stage process te
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A Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption increasing volume of data into cloud storage, where cloud providers require high levels of trust, and data breaches are significant problems. Encrypting the data with conventional schemes is considered the best option to avoid security problems. However, a decryption process is necessary when the d
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