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New frontiers in high performance computing and big data

Record Type:	Language materials, printed : Monograph/item
Title/Author:	New frontiers in high performance computing and big data/ edited by Geoffrey Fox, Vladimir Getov, Lucio Grandinetti, Gerhard Joubert, and Thomas Sterling.
other author:	Sterling, Thomas Lawrence,
corporate name:	WHO Expert Committee on Specifications for Pharmaceutical Preparations
Published:	Amsterdam :IOS Press, : [2017].,
Description:	1 online resource (viii, 263 p.)
Notes:	Includes indexes.
基督教聖經之智慧書導讀 :	For the last four decades, parallel computing platforms have increasingly formed the basis for the development of high performance systems primarily aimed at the solution of intensive computing problems, and the application of parallel computing systems has also become a major factor in furthering scientific research. But such systems also offer the possibility of solving the problems encountered in the processing of large-scale scientific data sets, as well as in the analysis of Big Data in the fields of medicine, social media, marketing, economics etc.This book presents papers from the International Research Workshop on Advanced High Performance Computing Systems, held in Cetraro, Italy, in July 2016. The workshop covered a wide range of topics and new developments related to the solution of intensive and large-scale computing problems, and the contributions included in this v. cover aspects of the evolution of parallel platforms and highlight some of the problems encountered with the development of ever more powerful computing systems. The importance of future large-scale data science applications is also discussed.The book will be of particular interest to all those involved in the development or application of parallel computing systems.--Provided by publisher.
Subject:	Big data - Congresses. -
Online resource:	http://ebooks.windeal.com.tw/ios/cover.asp?isbn=9781614998150
ISBN:	9781614998167 (e-book)

New frontiers in high performance computing and big data
New frontiers in high performance computing and big data[electronic resource] /edited by Geoffrey Fox, Vladimir Getov, Lucio Grandinetti, Gerhard Joubert, and Thomas Sterling. - Amsterdam :IOS Press,[2017]. - 1 online resource (viii, 263 p.) - Advances in parallel computing ;v. 30. - Advances in parallel computing ;v. 26..

Includes indexes.

Pt. 1. Architectures -- Exascale Co-Design Progress and Accomplishments -- Application-Specific Energy Modeling of Multi-Core Processors -- Asymptotic Computing--Undoing the Damage -- MPI Streams for HPC Applications -- Advances in Topology-Aware Scheduling in Multidimensional Torus-Based Systems -- Pt. 2. Data Science -- Hide Subsections Patterns for High Performance Computing and Big Data: State of the Art and Semantic Approaches -- Big Iron, Big Data, and Big Identity -- Programming Big Data Analysis on Clouds and Extreme Scale Systems -- Adaptive Data Placement in Multi-Tiered Data Staging Runtime -- Pt. 3. Case Studies -- Hide Subsections Parallelizing Big Data Machine Learning Applications with Model Rotation -- How Data Analysis Supports Crime Prediction in Smart Cities -- Merging Data Science and Large Scale Computational Modeling -- Optimizing Science Applications for the Cori, Knights Landing, System at NERSC -- Quantum Annealing and the Satisfiability Problem.

For the last four decades, parallel computing platforms have increasingly formed the basis for the development of high performance systems primarily aimed at the solution of intensive computing problems, and the application of parallel computing systems has also become a major factor in furthering scientific research. But such systems also offer the possibility of solving the problems encountered in the processing of large-scale scientific data sets, as well as in the analysis of Big Data in the fields of medicine, social media, marketing, economics etc.This book presents papers from the International Research Workshop on Advanced High Performance Computing Systems, held in Cetraro, Italy, in July 2016. The workshop covered a wide range of topics and new developments related to the solution of intensive and large-scale computing problems, and the contributions included in this v. cover aspects of the evolution of parallel platforms and highlight some of the problems encountered with the development of ever more powerful computing systems. The importance of future large-scale data science applications is also discussed.The book will be of particular interest to all those involved in the development or application of parallel computing systems.--Provided by publisher.

ISBN: 9781614998167 (e-book)Subjects--Topical Terms:

408058
Big data
--Congresses.

LC Class. No.: QA76.88 / .I574 2016eb

Dewey Class. No.: 004.1/1

New frontiers in high performance computing and big data
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520 $a For the last four decades, parallel computing platforms have increasingly formed the basis for the development of high performance systems primarily aimed at the solution of intensive computing problems, and the application of parallel computing systems has also become a major factor in furthering scientific research. But such systems also offer the possibility of solving the problems encountered in the processing of large-scale scientific data sets, as well as in the analysis of Big Data in the fields of medicine, social media, marketing, economics etc.This book presents papers from the International Research Workshop on Advanced High Performance Computing Systems, held in Cetraro, Italy, in July 2016. The workshop covered a wide range of topics and new developments related to the solution of intensive and large-scale computing problems, and the contributions included in this v. cover aspects of the evolution of parallel platforms and highlight some of the problems encountered with the development of ever more powerful computing systems. The importance of future large-scale data science applications is also discussed.The book will be of particular interest to all those involved in the development or application of parallel computing systems.--Provided by publisher.
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