EAN: 9783031006210

SYNTHESIS LECTURES ON COMPUTER ARCHITECTURE A...

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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