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Titlebook: Analyzing Analytics; Rajesh Bordawekar,Bob Blainey,Ruchir Puri Book 2016 Springer Nature Switzerland AG 2016

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發(fā)表于 2025-3-21 17:55:57 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Analyzing Analytics
影響因子2023Rajesh Bordawekar,Bob Blainey,Ruchir Puri
視頻videohttp://file.papertrans.cn/157/156808/156808.mp4
學科分類Synthesis Lectures on Computer Architecture
圖書封面Titlebook: Analyzing Analytics;  Rajesh Bordawekar,Bob Blainey,Ruchir Puri Book 2016 Springer Nature Switzerland AG 2016
影響因子This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads
Pindex Book 2016
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沙發(fā)
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Architectural Desiderata for Analytics,plars, and discussed various acceleration opportunities. In this chapter, we discuss future trends in analytics workloads and its impact on designing future processors and systems specifically targeted for analytics.
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1935-3235 mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and da
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Erla Hallsteinsdóttir,Anke Heierplars, and discussed various acceleration opportunities. In this chapter, we discuss future trends in analytics workloads and its impact on designing future processors and systems specifically targeted for analytics.
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Synthesis Lectures on Computer Architecturehttp://image.papertrans.cn/a/image/156808.jpg
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