Description
| Product ID: | 9780262046855 |
| Product Form: | Hardback |
| Country of Manufacture: | GB |
| Series: | Scientific and Engineering Computation |
| Title: | High Performance Big Data Computing |
| Authors: | Author: Dhabaleswar K. Panda, Xiaoyi Lu |
| Page Count: | 288 |
| Subjects: | Artificial intelligence, Artificial intelligence |
| Description: | Select Guide Rating An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. 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. |
| Imprint Name: | MIT Press |
| Publisher Name: | MIT Press Ltd |
| Country of Publication: | GB |
| Publishing Date: | 2022-08-02 |
