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.
About the Editor
Dr. Chao Wang is an Associate Professor in the School of Computer Science at the University of Science and Technology of China. He is the Associate Editor of ACM Transactions on Design Automations for Electronics Systems (TODAES), Applied Soft Computing, Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics. Dr. Chao Wang was the recipient of Youth Innovation Promotion Association, CAS, ACM China Rising Star Honorable Mention (2016), and best IP nomination of DATE 2015. He is now on the CCF Technical Committee on Computer Architecture, CCF Task Force on Formal Methods. He is a Senior Member of IEEE, Senior Member of CCF, and a Senior Member of ACM.
Edited by:
Chao Wang
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions:
Height: 254mm,
Width: 178mm,
Weight: 453g
ISBN: 9780367572891
ISBN 10: 0367572893
Series: Chapman & Hall/CRC Big Data Series
Pages: 286
Publication Date: 30 June 2020
Audience:
Professional and scholarly
,
Undergraduate
Format: Paperback
Publisher's Status: Active
Section I Big Data Architectures Chapter 1 ◾ Dataflow Model for Cloud Computing Frameworks in Big Data Dong Dai, Yong Chen, and Gangyong Jia Chapter 2 ◾ Design of a Processor Core Customized for Stencil Computation Youyang Zhang, Yanhua Li, and Youhui Zhang Chapter 3 ◾ Electromigration Alleviation Techniques for 3D Integrated Circuits Yuanqing Cheng, Aida Todri-Sanial, Alberto Bosio, Luigi Dilillo, Patrick Girard, Arnaud Virazel, Pascal Vivet, and Marc Belleville Chapter 4 ◾ A 3D Hybrid Cache Design for CMP Architecture for Data-Intensive Applications Ing-Chao Lin, Jeng-Nian Chiou, and Yun-Kae Law Section II Emerging Big Data Applications Chapter 5 ◾ Matrix Factorization for Drug–Target Interaction Prediction Yong Liu, Min Wu, Xiao-Li Li, and Peilin Zhao Chapter 6 ◾ Overview of Neural Network Accelerators Yuntao Lu, Chao Wang, Lei Gong, Xi Li, Aili Wang, and Xuehai Zhou Chapter 7 ◾ Acceleration for Recommendation Algorithms in Data Mining Chongchong Xu, Chao Wang, Lei Gong, Xi Li, Aili Wang, and Xuehai Zhou Chapter 8 ◾ Deep Learning Accelerators Yangyang Zhao, Chao Wang, Lei Gong, Xi Li, Aili Wang, and Xuehai Zhou Chapter 9 ◾ Recent Advances for Neural Networks Accelerators and Optimizations Fan Sun, Chao Wang, Lei Gong, Xi Li, Aili Wang, and Xuehai Zhou Chapter 10 ◾ Accelerators for Clustering Applications in Machine Learning Yiwei Zhang, Chao Wang, Lei Gong, Xi Li, Aili Wang, and Xuehai Zhou Chapter 11 ◾ Accelerators for Classification Algorithms in Machine Learning Shiming Lei, Chao Wang, Lei Gong, Xi Li, Aili Wang, and Xuehai Zhou Chapter 12 ◾ Accelerators for Big Data Genome Sequencing Haijie Fang, Chao Wang, Shiming Lei, Lei Gong, Xi Li, Aili Wang, and Xuehai Zhou
Prof. Chao Wang received B.S. and Ph.D. degrees from School of Computer Science, University of Science and Technology of China, in 2006 and 2011 respectively. He has been a postdoctoral researcher in USTC from 2011 to 2013. He also worked with Infineon Technologies A.G. in 2007-2008. He is the associate editor of Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics.
