1st Edition
Parallel Computing Architectures and APIs IoT Big Data Stream Processing
Parallel Computing Architectures and APIs: IoT Big Data Stream Processing commences from the point high-performance uniprocessors were becoming increasingly complex, expensive, and power-hungry. A basic trade-off exists between the use of one or a small number of such complex processors, at one extreme, and a moderate to very large number of simpler processors, at the other. When combined with a high-bandwidth, interprocessor communication facility leads to significant simplification of the design process. However, two major roadblocks prevent the widespread adoption of such moderately to massively parallel architectures: the interprocessor communication bottleneck, and the difficulty and high cost of algorithm/software development.
One of the most important reasons for studying parallel computing architectures is to learn how to extract the best performance from parallel systems. Specifically, you must understand its architectures so that you will be able to exploit those architectures during programming via the standardized APIs.
This book would be useful for analysts, designers and developers of high-throughput computing systems essential for big data stream processing emanating from IoT-driven cyber-physical systems (CPS).
This pragmatic book:
- Devolves uniprocessors in terms of a ladder of abstractions to ascertain (say) performance characteristics at a particular level of abstraction
- Explains limitations of uniprocessor high performance because of Moore’s Law
- Introduces basics of processors, networks and distributed systems
- Explains characteristics of parallel systems, parallel computing models and parallel algorithms
- Explains the three primary categorical representatives of parallel computing architectures, namely, shared memory, message passing and stream processing
- Introduces the three primary categorical representatives of parallel programming APIs, namely, OpenMP, MPI and CUDA
- Provides an overview of Internet of Things (IoT), wireless sensor networks (WSN), sensor data processing, Big Data and stream processing
- Provides introduction to 5G communications, Edge and Fog computing
Parallel Computing Architectures and APIs: IoT Big Data Stream Processing discusses stream processing that enables the gathering, processing and analysis of high-volume, heterogeneous, continuous Internet of Things (IoT) big data streams, to extract insights and actionable results in real time. Application domains requiring data stream management include military, homeland security, sensor networks, financial applications, network management, web site performance tracking, real-time credit card fraud detection, etc.
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).
