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Heterogeneous Computing with OpenCL 2.0,

Edition 1

Editors: By David R. Kaeli, Perhaad Mistry, Dana Schaa and Dong Ping Zhang

Publication Date: 18 May 2015

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Heterogeneous Computing with OpenCL 2.0 teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs). This fully-revised edition includes the latest enhancements in OpenCL 2.0 including:

• Shared virtual memory to increase programming flexibility and reduce data transfers that consume resources
• Dynamic parallelism which reduces processor load and avoids bottlenecks
• Improved imaging support and integration with OpenGL

Designed to work on multiple platforms, OpenCL will help you more effectively program for a heterogeneous future. Written by leaders in the parallel computing and OpenCL communities, this book explores memory spaces, optimization techniques, extensions, debugging and profiling. Multiple case studies and examples illustrate high-performance algorithms, distributing work across heterogeneous systems, embedded domain-specific languages, and will give you hands-on OpenCL experience to address a range of fundamental parallel algorithms.

Key Features

Updated content to cover the latest developments in OpenCL 2.0, including improvements in memory handling, parallelism, and imaging support
Explanations of principles and strategies to learn parallel programming with OpenCL, from understanding the abstraction models to thoroughly testing and debugging complete applications
Example code covering image analytics, web plugins, particle simulations, video editing, performance optimization, and more

About the author

By David R. Kaeli, Northeastern University, Boston, MA, USA; Perhaad Mistry, Northeastern University, Boston, MA, USA; Dana Schaa, Northeastern University, Boston, MA, USA and Dong Ping Zhang, AMD, Sunnyvale, California, USA

List of Figures
List of Tables
Foreword
Acknowledgments
Chapter 1: Introduction
- Abstract
- 1.1 Introduction to Heterogeneous Computing
- 1.2 The Goals of This Book
- 1.3 Thinking Parallel
- 1.4 Concurrency and Parallel Programming Models
- 1.5 Threads and Shared Memory
- 1.6 Message-Passing Communication
- 1.7 Different Grains of Parallelism
- 1.8 Heterogeneous Computing with OpenCL
- 1.9 Book Structure
Chapter 2: Device architectures
- Abstract
- 2.1 Introduction
- 2.2 Hardware Trade-offs
- 2.3 The Architectural Design Space
- 2.4 Summary
Chapter 3: Introduction to OpenCL
- Abstract
- 3.1 Introduction
- 3.2 The OpenCL Platform Model
- 3.3 The OpenCL Execution Model
- 3.4 Kernels and the OpenCL Programming Model
- 3.5 OpenCL Memory Model
- 3.6 The OpenCL Runtime with an Example
- 3.7 Vector Addition Using an OpenCL C++ Wrapper
- 3.8 OpenCL for CUDA Programmers
- 3.9 Summary
Chapter 4: Examples
- Abstract
- 4.1 OpenCL Examples
- 4.2 Histogram
- 4.3 Image Rotation
- 4.4 Image Convolution
- 4.5 Producer-Consumer
- 4.6 Utility Functions
- 4.7 Summary
Chapter 5: OpenCL runtime and concurrency model
- Abstract
- 5.1 Commands and the Queuing Model
- 5.2 Multiple Command-Queues
- 5.3 The Kernel Execution Domain: Work-Items, Work-Groups, and NDRanges
- 5.4 Native and Built-In Kernels
- 5.5 Device-Side Queuing
- 5.6 Summary
Chapter 6: OpenCL host-side memory model
- Abstract
- 6.1 Memory Objects
- 6.2 Memory Management
- 6.3 Shared Virtual Memory
- 6.4 Summary
Chapter 7: OpenCL device-side memory model
- Abstract
- 7.1 Synchronization and Communication
- 7.2 Global Memory
- 7.3 Constant Memory
- 7.4 Local Memory
- 7.5 Private Memory
- 7.6 Generic Address Space
- 7.7 Memory Ordering
- 7.8 Summary
Chapter 8: Dissecting OpenCL on a heterogeneous system
- Abstract
- 8.1 OpenCL on an AMD FX-8350 CPU
- 8.2 OpenCL on the AMD Radeon R9 290X GPU
- 8.3 Memory Performance Considerations in OpenCL
- 8.4 Summary
Chapter 9: Case study: Image clustering
- Abstract
- 9.1 Introduction
- 9.2 The Feature Histogram on the CPU
- 9.3 OpenCL Implementation
- 9.4 Performance Analysis
- 9.5 Conclusion
Chapter 10: OpenCL profiling and debugging
- Abstract
- 10.1 Introduction
- 10.2 Profiling OpenCL Code Using Events
- 10.3 AMD CodeXL
- 10.4 Profiling Using CodeXL
- 10.5 Analyzing Kernels Using CodeXL
- 10.6 Debugging OpenCL Kernels Using CodeXL
- 10.7 Debugging Using printf
- 10.8 Summary
Chapter 11: Mapping high-level programming languages to OpenCL 2.0: A compiler writer’s perspective
- Abstract
- 11.1 Introduction
- 11.2 A Brief Introduction to C++ AMP
- 11.3 OpenCL 2.0 as a Compiler Target
- 11.4 Mapping Key C++ AMP Constructs to OpenCL
- 11.5 C++ AMP Compilation Flow
- 11.6 Compiled C++ AMP Code
- 11.7 How Shared Virtual Memory in OpenCL 2.0 Fits in
- 11.8 Compiler Support for Tiling in C++AMP
- 11.9 Address Space Deduction
- 11.10 Data Movement Optimization
- 11.11 Binomial Options: A Full Example
- 11.12 Preliminary Results
- 11.13 Conclusion
Chapter 12: WebCL: Enabling OpenCL acceleration of Web applications
- Abstract
- 12.1 Introduction
- 12.2 Programming with WebCL
- 12.3 Synchronization
- 12.4 Interoperability with WebGL
- 12.5 Example Application
- 12.6 Security Enhancement
- 12.7 WebCL on the Server
- 12.8 Status and Future of WebCL
- Works Cited
Chapter 13: Foreign lands: Plugging OpenCL in
- Abstract
- 13.1 Introduction
- 13.2 Beyond C and C+ +
- 13.3 Haskell OpenCL
- 13.4 Summary
Index

Book Reviews

"...one of the best sources to start with OpenCL…If you need to start writing parallel programs but are intimidated by the complexity, this book will not leave you any excuses!"—Computing Reviews

ISBN: 9780128014141

Page Count: 336

Retail Price :

Gaster et al, Heterogeneous Computing with OpenCL, Morgan Kaufmann, 2011, Paperback, 282pp, 9780123877666, $69.95
Herlihy: The Art of Multiprocessor Programming Revised Ed, Morgan Kaufmann, 2012, Paperback, 514pp, 9780123973375, $74.95

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