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Big data and HPC : = ecosystem and c...
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Grandinetti, Lucio, (1941-)
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Big data and HPC : = ecosystem and convergence
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Big data and HPC :/ edited by Lucio Grandinetti, Seyedeh Leili Mirtaheri, Reza Shahbazian, Thomas Sterling, and Vladimir Voevodin.
其他題名:
ecosystem and convergence
其他作者:
Grandinetti, Lucio,
出版者:
Amsterdam :IOS Press, : [2018],
面頁冊數:
1 online resource (viii, 325 p.) :ill.
附註:
Selected papers from the TopHPC2017 congress on Advances in High-Performance Computing and Big Data Analytics in the Exascale Era, held in Tehran, Iran, in April 2017.
內容註:
Intro; Title Page; Preface; Contents; State of the Art and Future Scenarios; Runtime System Architecture for Dynamic Adaptive Execution; High Performance Computing and Big Data Convergence: A Technical Review; Challenges in HPC Evaluation: Towards a Methodology for Scientific Application Requirements; Scaling Big Data Neuroscience: From Interactive Analytics to HPC Platforms; Big Data Challenges; CBIR on Big Data by Use of Deep Learning; APPGRIT: A Parallel Pipeline for Graph Representation in Text Mining; Introduction and Patent Analysis of Signal Processing for Big Data.
內容註:
Analysis and Design of IoT Based Physical Location Monitoring SystemAutonomous Task Scheduling for Fast Big Data Processing; Adaptive Resource Management for Distributed Data Analytics; HPC Challenges; High-Performance Massive Subgraph Counting Using Pipelined Adaptive-Group Communication; Final Parallel and Distributed Computing Assignment for Master Students: Description of the Properties and Parallel Structure of Algorithms; Parallel Motion Estimation Based on GPU and Combined GPU-CPU; GPU-Based Iterative Hill Climbing Algorithm to Solve Symmetric Traveling Salesman Problem.
內容註:
Reliability-Aware Voltage Scaling of Multicore Processors in Dark Silicon EraTime Collection: An Abstraction for Shared Objects in Parallel Programming; Parallel and Distributed Analysis of Microarray Data; Extracting Distributed Architecture from Source Code Using an Evolutionary Approach; An Architectural Approach to Grid Provisioning; Subject Index; Author Index.
標題:
Big data - Congresses. -
電子資源:
http://ebooks.windeal.com.tw/ios/cover.asp?isbn=9781614998815
ISBN:
9781614998822 (e-book)
Big data and HPC : = ecosystem and convergence
Big data and HPC :
ecosystem and convergence[electronic resource] /edited by Lucio Grandinetti, Seyedeh Leili Mirtaheri, Reza Shahbazian, Thomas Sterling, and Vladimir Voevodin. - Amsterdam :IOS Press,[2018] - 1 online resource (viii, 325 p.) :ill. - Advances in parallel computing,v. 330927-5452 ;. - Advances in parallel computing ;v. 33..
Selected papers from the TopHPC2017 congress on Advances in High-Performance Computing and Big Data Analytics in the Exascale Era, held in Tehran, Iran, in April 2017.
Includes bibliographical references.
Intro; Title Page; Preface; Contents; State of the Art and Future Scenarios; Runtime System Architecture for Dynamic Adaptive Execution; High Performance Computing and Big Data Convergence: A Technical Review; Challenges in HPC Evaluation: Towards a Methodology for Scientific Application Requirements; Scaling Big Data Neuroscience: From Interactive Analytics to HPC Platforms; Big Data Challenges; CBIR on Big Data by Use of Deep Learning; APPGRIT: A Parallel Pipeline for Graph Representation in Text Mining; Introduction and Patent Analysis of Signal Processing for Big Data.
ISBN: 9781614998822 (e-book)
LCCN: 2018952148Subjects--Topical Terms:
3166510
Big data
--Congresses.
LC Class. No.: QA76.9.B45 / B52 2018
Dewey Class. No.: 005.7
Big data and HPC : = ecosystem and convergence
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Intro; Title Page; Preface; Contents; State of the Art and Future Scenarios; Runtime System Architecture for Dynamic Adaptive Execution; High Performance Computing and Big Data Convergence: A Technical Review; Challenges in HPC Evaluation: Towards a Methodology for Scientific Application Requirements; Scaling Big Data Neuroscience: From Interactive Analytics to HPC Platforms; Big Data Challenges; CBIR on Big Data by Use of Deep Learning; APPGRIT: A Parallel Pipeline for Graph Representation in Text Mining; Introduction and Patent Analysis of Signal Processing for Big Data.
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Analysis and Design of IoT Based Physical Location Monitoring SystemAutonomous Task Scheduling for Fast Big Data Processing; Adaptive Resource Management for Distributed Data Analytics; HPC Challenges; High-Performance Massive Subgraph Counting Using Pipelined Adaptive-Group Communication; Final Parallel and Distributed Computing Assignment for Master Students: Description of the Properties and Parallel Structure of Algorithms; Parallel Motion Estimation Based on GPU and Combined GPU-CPU; GPU-Based Iterative Hill Climbing Algorithm to Solve Symmetric Traveling Salesman Problem.
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Reliability-Aware Voltage Scaling of Multicore Processors in Dark Silicon EraTime Collection: An Abstraction for Shared Objects in Parallel Programming; Parallel and Distributed Analysis of Microarray Data; Extracting Distributed Architecture from Source Code Using an Evolutionary Approach; An Architectural Approach to Grid Provisioning; Subject Index; Author Index.
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