£111.36

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Springer Distributed Graph Analytics: Programming & Compilation

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Description

Distributed Graph Analytics explores the convergence of graph algorithms and high-performance computing, providing a comprehensive guide to efficient and scalable execution of graph processing applications in data and network analysis. This Springer publication addresses innovations across multiple levels: algorithms, data structures, implementation strategies, and hardware tuning. The book emphasises how programming languages and compilers automate efficient code generation for diverse architectures. Organised into three focused parts covering programming, languages, and compilation techniques, this resource equips professionals and researchers with practical knowledge for optimising graph analytics workloads. Whether you're developing data analysis tools, building network processing systems, or advancing compiler technology, this book offers essential insights into bridging the gap between algorithmic theory and real-world high-performance execution on modern computing platforms.

Key Features

Covers graph algorithms and high-performance computing integration for scalable data and network analysis applications.

Explores programming languages and compiler design specifically tailored for automated efficient code generation across various architectures.

Addresses implementation strategies and hardware tuning techniques essential for optimising distributed graph processing systems.

Divided into three comprehensive parts focusing on programming, languages, and compilation methodologies for graph analytics.

Published by Springer, offering rigorous academic and practical guidance for researchers and software engineers.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
18 April 2021
Listed Since
22 March 2021

Barcode

No barcode data available

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