Parallel Computing Architectures and APIs IoT Big Data Stream Processing

1 Uniprocessor Computers     
1.1 Type of Computers
1.2 Computer System
1.3 Hardware and software logical equivalence
1.4 Stack of Abstraction
1.5 Application Programming Interfaces (APIs)
1.6 Summary

2 Processor Physics and Moore’s Law       
2.1 Speed of processing and Power Problem
2.2 Area, Delay and Power Consumption
2.3 Area, Latency and Power tradeoffs
2.4 Moore’s Law
2.5 Performance Wall
2.6 Summary

Section I Genesis of Parallel Computing    

3 Processor Basics       
3.1 Processor
3.2 Aspects of processor performance
3.3 Enhancing uniprocessor performance
3.4 Summary

4 Networking Basics        
4.1 Network Principles
4.2 Types of Networks
4.3 Network Models
4.4 Interconnection Networks
4.4.1 Ethernet
4.4.2 Switches
4.5 Summary

5 Distributed Systems Basics        
5.1 Distributed Systems
5.2 Distributed system benefits
5.3 Distributed Computation Systems
5.4 Summary

Section II Road to Parallel Computing    
6 Parallel Systems        
6.1 Flynn’s taxonomy for parallel computer architectures
6.2 Types of parallel computers
6.3 Characteristics of parallel systems
6.5 Summary

7 Parallel Computing Models     
7.1 Shared Memory Models
7.2 Interconnection Network Models
7.3 Dataflow Model
7.4 Summary

8 Parallel Algorithms      
8.1 Classes of Problems solvable through parallelization
8.2 Types of Parallelization
8.3 Granularity of Parallelization
8.4 Assigning computational tasks to processors
8.5 Illustrating design of a parallel algorithm
8.6 Parallel Algorithms for Conventional Computations
8.6.1 Parallel Prefix and Suffix Computations on a
Linked List
8.7 Parallel Algorithms for Unconventional Computations
8.8 Summary

Section III Parallel Computing Architectures   

9 Parallel Computing Architecture Basics   
9.1 High Performance Distributed Computing
9.2 Performance evaluation
9.3 Application and Architecture
9.4 Maximum performance computing approach
9.5 Parallel computing basics
9.6 Parallel computing paradigms
9.7 Summary

10 Shared-memory Architecture     
10.1 Shared memory paradigm
10.2 Cache
10.3 Write policy
10.4 Cache coherency
10.5 Memory consistency
10.6 Summary

11 Messaging Passing Architecture    
11.1 Message passing paradigm
11.2 Routing
11.3 Switching
11.4 Summary

12 Stream Processing Architecture    
12.1 Data Flow Paradigm
12.2 Parallel Accelerators
12.3 Stream Processors
12.4 Summary

Section IV Parallel Computing APIs     

13 Parallel Computing Programming Basics   
13.1 Shared Memory Programming
13.2 Message Passing Programming
13.3 Stream Programming
13.4 Summary
Appendix 13A Functional Programming   
Appendix 13B MapReduce     

14 Shared-memory Parallel Programming with OpenMP 
14.1 OpenMP
14.2 Overview of features
14.3 Additional feature details
14.4 Summary

15 Message Passing Parallel Programming with MPI
15.1 Introduction to MPI
15.2 Basic point-to-point communication routines
15.3 Basic MPI collective communication routines
15.4 Environment management routines
15.5 Point to point communication routines
15.6 Collective communication routines
15.7 Summary

16 Stream Processing Programming with CUDA, OpenCL 20
  and OpenACC
16.1 CUDA
16.2 OpenCL
16.3 OpenACC
16.4 Summary

Section V IoT Big Data Stream Processing    

17 Internet of Things Computing      
17.1 Introduction to Internet of Things
17.2 RFID (Radio Frequency Identification)
17.3 Sensor Networks
17.4 Summary
Appendix 17A Internet of Things (IoT) in 5G Mobile    
Technologies
Appendix 17B Edge and Fog Computing    

18 Sensor Data Processing      
18.1 Sensor Data-Gathering and Data-Dissemination
Mechanisms
18.2 Time Windows
18.3 Sensor Database
18.4 Data-Fusion Mechanisms
18.5 Data Fusion Techniques, Methods, and Algorithms
18.6 Data Fusion Architectures and Models
18.7 Summary
Appendix 18A Wireless Sensor Networks (WAN)         
Anomalies    

19 Big Data Computing      
19.1 Introduction to Big Data
19.2 Tools, Techniques and Technologies of Big Data
19.3 NoSQL Databases
19.4 Aadhaar Project
19.5 Summary
Appendix 19A Compute-intensive Big Compute   
versus data-intensive Big Data

20 Stream Processing      
20.1 Big Data Stream Processing
20.2 Stream Processing System Implementations

1. TelegraphCQ
2. STREAM
3. Aurora
4. Borealis
5. IBM SYSTEM S AND IBM SPADE

1. Apache Storm
2. Yahoo! S4
3. Apache Samza
4. Apache Streaming

20.3 Summary
Appendix 20A Spark      

Epilogue: Quantum Computing      

Bibliography

Index

Biography

Vivek Kale has more than two decades of professional IT experience during which he has handled and consulted on various aspects of enterprise-wide information modeling, enterprise architectures, business process re-design, and, e-business architectures. He has been Group CIO of　Essar Group, the　steel/oil & gas major of India,　as well as,　Raymond Ltd., the textile & apparel major of India. He is a seasoned practitioner in enhancing business agility through digital transformation of business models, enterprise architecture and business processes, and, enhancing IT-enabled enterprise intelligence (EQ). He has authored books on Cloud Computing and Big Data Computing. He is also author of Big Data Computing: A Guide for Business and Technology Managers(CRC Press, 2016), Agile Network Businesses: Collaboration, Coordination, and Competitive Advantage (CRC Press 2017), and, Digital Transformation of Enterprise Architecture (CRC Press 2020).