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English | 2018 | ISBN: 1498783996 | 286 Pages | True PDF | 7.9 MB
High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering.
The book is organized into two main sections. The first section covers Big Data architectures, including cloud computing systems, and heterogeneous accelerators. It also covers emerging 3D IC design principles for memory architectures and devices. The second section of the book illustrates emerging and practical applications of Big Data across several domains, including bioinformatics, deep learning, and neuromorphic engineering.
Features:
- Covers a wide range of Big Data architectures, including distributed systems like Hadoop/Spark
- Includes accelerator-based approaches for big data applications such as GPU-based acceleration techniques, and hardware acceleration such as FPGA/CGRA/ASICs
- Presents emerging memory architectures and devices such as NVM, STT- RAM, 3D IC design principles
- Describes advanced algorithms for different big data application domains
- Illustrates novel analytics techniques for Big Data applications, scheduling, mapping, and partitioning methodologies
Featuring contributions from leading experts, this book presents state-of-the-art research on the methodologies and applications of high-performance computing for big data applications.
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