High Performance Computing for Big Data: Methodologies and Applications

Organized into four main sections, this book will present state-of-the-art research, methodologies, and applications of high performance computing for big data applications. It will cover fundamental issues in Big Data research, 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, finance, life sciences, and neuromorphic engineering.

Table of Contents

SECTION I Big Data Architectures
CHAPTER 1 Dataflow Model for Cloud Computing Frameworks in Big Data
CHAPTER 2 Design of a Processor Core Customized for Stencil Computation
CHAPTER 3 Electromigration Alleviation Techniques for 3D Integrated Circuits
CHAPTER 4 A 3D Hybrid Cache Design for CMP Architecture for Data-Intensive Applications

SECTION II Emerging Big Data Applications
CHAPTER 5 Matrix Factorization for Drug-Target Interaction Prediction
CHAPTER 6 Overview of Neural Network Accelerators
CHAPTER 7 Acceleration for Recommendation Algorithms in Data Mining
CHAPTER 8 Deep Learning Accelerators
CHAPTER 9 Recent Advances for Neural Networks Accelerators and Optimizations
CHAPTER 10 Accelerators for Clustering Applications in Machine Learning
CHAPTER 11 Accelerators for Classification Algorithms in Machine Learning
CHAPTER 12 Accelerators for Big Data Genome Sequencing