High Performance Computing for Big Data: Methodologies and Applications

375 Pages·2017·9.56 MB·English

by  Chao Wang

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Chapman & Hall/CRC
Big Data Series
SERIES EDITOR
Sanjay Ranka
AIMS AND SCOPE
This series aims to present new research and applications in
Big Data, along with the computational tools and
techniques currently in development. The inclusion of
concrete examples and applications is highly encouraged.
The scope of the series includes, but is not limited to, titles
in the areas of social networks, sensor networks, data-
centric computing, astronomy, genomics, medical data
analytics, large-scale e-commerce, and other relevant topics
that may be proposed by potential contributors.
PUBLISHED TITLES
HIGH PERFORMANCE COMPUTING FOR BIG DATA
Chao Wang
FRONTIERS IN DATA SCIENCE
Matthias Dehmer and Frank Emmert-Streib
BIG DATA MANAGEMENT AND PROCESSING
Kuan-Ching Li, Hai Jiang, and Albert Y. Zomaya
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BIG DATA COMPUTING: A GUIDE FOR BUSINESS
AND TECHNOLOGY MANAGERS
Vivek Kale
BIG DATA IN COMPLEX AND SOCIAL NETWORKS
My T. Thai, Weili Wu, and Hui Xiong
BIG DATA OF COMPLEX NETWORKS
Matthias Dehmer, Frank Emmert-Streib, Stefan Pickl, and
Andreas Holzinger
BIG DATA : ALGORITHMS, ANALYTICS, AND
APPLICATIONS
Kuan-Ching Li, Hai Jiang, Laurence T. Yang, and Alfredo
Cuzzocrea
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Contents
Preface
Acknowledgments
Editor
Contributors
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
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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
INDEX
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Preface
AS SCIENTIFIC APPLICATIONS HAVE become more data intensive, the
management of data resources and dataflow between the storage and
computing resources is becoming a bottleneck. Analyzing, visualizing, and
managing these large data sets is posing significant challenges to the research
community. The conventional parallel architecture, systems, and software
will exceed the performance capacity with this expansive data scale. At
present, researchers are increasingly seeking a high level of parallelism at the
data level and task level using novel methodologies for emerging
applications. A significant amount of state-of-the-art research work on big
data has been executed in the past few years.
This book presents the contributions of leading experts in their respective
fields. It covers fundamental issues about Big Data, including 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:
1. “Big Data Architectures” considers the research issues related to the
state-of-the-art architectures of big data, including cloud computing
systems and heterogeneous accelerators. It also covers emerging 3D
integrated circuit design principles for memory architectures and
devices.
2. “Emerging Big Data Applications” illustrates practical applications of
big data across several domains, including bioinformatics, deep
learning, and neuromorphic engineering.
Overall, the book reports on state-of-the-art studies and achievements in
methodologies and applications of high-performance computing for big data
applications.
The first part includes four interesting works on big data architectures. The
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