
High-Performance Big Data Computing
Description
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Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions.
The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.
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Content
1 Introduction 1
2 Parallel Programming Models and Systems 13
3 Parallel and Distributed Storage Systems 37
4 HPC Architectures and Trends 61
5 Opportunities and Challenges in Accelerating Big Data Computing 93
6 Benchmarking Big Data Systems 107
7 Accelerations with RDMA 121
8 Accelerations with Multicore/Accelerator Technologies 145
9 Acceleration with High-Performance Storage Technologies 159
10 Deep Learning over Big Data 175
11 Designs with Cloud Technologies 195
12 Frontier Research on High-Performance Big Data Computing 215
References 227
Index 257
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