找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Conquering Big Data with High Performance Computing; Ritu Arora Book 2016 Springer International Publishing Switzerland 2016 Big data.High

[复制链接]
查看: 32702|回复: 56
发表于 2025-3-21 19:01:48 | 显示全部楼层 |阅读模式
书目名称Conquering Big Data with High Performance Computing
编辑Ritu Arora
视频video
概述Presents curated information on state-of-the-practice in conquering Big Data challenges by leveraging HPC/HTC.Covers the landscape of open-science datacenters & resources; very useful for those profes
图书封面Titlebook: Conquering Big Data with High Performance Computing;  Ritu Arora Book 2016 Springer International Publishing Switzerland 2016 Big data.High
描述This book provides an overview of the resources and research projects that are bringing Big Data and High Performance Computing (HPC) on converging tracks. It demystifies Big Data and HPC for the reader by covering the primary resources, middleware, applications, and tools that enable the usage of HPC platforms for Big Data management and processing..Through interesting use-cases from traditional and non-traditional HPC domains, the book highlights the most critical challenges related to Big Data processing and management, and shows ways to mitigate them using HPC resources. Unlike most books on Big Data, it covers a variety of alternatives to Hadoop, and explains the differences between HPC platforms and Hadoop..Written by professionals and researchers in a range of departments and fields, this book is designed for anyone studying Big Data and its future directions. Those studying HPC will also find the content valuable..
出版日期Book 2016
关键词Big data; High performance computing; High throughput Computing; Data-Intensive computing; E-Discovery; P
版次1
doihttps://doi.org/10.1007/978-3-319-33742-5
isbn_softcover978-3-319-81589-3
isbn_ebook978-3-319-33742-5
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

书目名称Conquering Big Data with High Performance Computing影响因子(影响力)




书目名称Conquering Big Data with High Performance Computing影响因子(影响力)学科排名




书目名称Conquering Big Data with High Performance Computing网络公开度




书目名称Conquering Big Data with High Performance Computing网络公开度学科排名




书目名称Conquering Big Data with High Performance Computing被引频次




书目名称Conquering Big Data with High Performance Computing被引频次学科排名




书目名称Conquering Big Data with High Performance Computing年度引用




书目名称Conquering Big Data with High Performance Computing年度引用学科排名




书目名称Conquering Big Data with High Performance Computing读者反馈




书目名称Conquering Big Data with High Performance Computing读者反馈学科排名




单选投票, 共有 1 人参与投票
 

0票 0.00%

Perfect with Aesthetics

 

1票 100.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

0票 0.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 23:06:42 | 显示全部楼层
发表于 2025-3-22 00:32:00 | 显示全部楼层
Data Movement in Data-Intensive High Performance Computing,, two key metrics that determine the cost of moving data, have degraded significantly relative to processor cycle time and execution rate. Despite the limitation of sub-micron processor technology and the end of Dennard scaling, this trend will continue in the short-term making data movement a perfo
发表于 2025-3-22 07:39:48 | 显示全部楼层
发表于 2025-3-22 08:42:56 | 显示全部楼层
发表于 2025-3-22 13:23:28 | 显示全部楼层
Big Data Behind Big Data, large. In this chapter, we will examine two aspects of High Performance Computing (HPC) data that fall under the category of big data. The first is the collection of HPC environmental data and its analysis. The second is the collection of information on how large datasets are produced by scientific
发表于 2025-3-22 20:20:52 | 显示全部楼层
发表于 2025-3-22 22:01:18 | 显示全部楼层
Big Data Techniques as a Solution to Theory Problems,description of this approach as well as some examples. This approach is ideally suited for solving nonconvex optimization problems, multiobjective programming problems, models with a large degree of heterogeneity, rich policy structure, potential model uncertainty, and potential policy objective unc
发表于 2025-3-23 03:19:20 | 显示全部楼层
High-Frequency Financial Statistics Through High-Performance Computing,ion of securities and derivatives, and various business and economic analytics. Portfolio allocation is one of the most important problems in financial risk management. One most challenging part in portfolio allocation is the tremendous amount of data and the optimization procedures that require com
发表于 2025-3-23 05:49:17 | 显示全部楼层
Large-Scale Multi-Modal Data Exploration with Human in the Loop,multi-modal data streams. In this chapter we present a research framework for analyzing and mining such data streams at large-scale; we exploit parallel sequential pattern mining and iterative MapReduce in particular to enable human-in-the-loop large-scale data exploration powered by High Performanc
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-7-1 05:44
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表