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Analyzing Analytics [electronic resource] / by Rajesh Bordawekar, Bob Blainey, Ruchir Puri.

By: Bordawekar, Rajesh [author.].
Contributor(s): Blainey, Bob [author.] | Puri, Ruchir [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Computer Architecture: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Edition: 1st ed. 2016.Description: X, 118 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031017490.Subject(s): Electronic circuits | Microprocessors | Computer architecture | Electronic Circuits and Systems | Processor ArchitecturesAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 621.3815 Online resources: Click here to access online
Contents:
Introduction -- Overview of Analytics Exemplars -- Accelerating Analytics -- Accelerating Analytics in Practice: Case Studies -- Architectural Desiderata for Analytics -- Bibliography -- Authors' Biographies .
In: Springer Nature eBookSummary: 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, impact on programming models and runtime systems, and designing analytics systems.
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Introduction -- Overview of Analytics Exemplars -- Accelerating Analytics -- Accelerating Analytics in Practice: Case Studies -- Architectural Desiderata for Analytics -- Bibliography -- Authors' Biographies .

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, impact on programming models and runtime systems, and designing analytics systems.

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