Heterogeneous Computing with OpenCL: Revised OpenCL 1.2 Edition [NOOK Book]

Overview

Heterogeneous Computing with OpenCL 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) such as AMD Fusion technology. Designed to work on multiple platforms and with wide industry support, OpenCL will help you more effectively program for a heterogeneous future.

Written by leaders in the parallel computing and OpenCL communities, this book ...

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Heterogeneous Computing with OpenCL: Revised OpenCL 1.2 Edition

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Overview

Heterogeneous Computing with OpenCL 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) such as AMD Fusion technology. Designed to work on multiple platforms and with wide industry support, OpenCL will help you more effectively program for a heterogeneous future.

Written by leaders in the parallel computing and OpenCL communities, this book will give you hands-on OpenCL experience to address a range of fundamental parallel algorithms. The authors explore memory spaces, optimization techniques, graphics interoperability, extensions, and debugging and profiling. Intended to support a parallel programming course, Heterogeneous Computing with OpenCL includes detailed examples throughout, plus additional online exercises and other supporting materials.



  • Explains principles and strategies to learn parallel programming with OpenCL, from understanding the four abstraction models to thoroughly testing and debugging complete applications.
  • Covers image processing, web plugins, particle simulations, video editing, performance optimization, and more.
  • Shows how OpenCL maps to an example target architecture and explains some of the tradeoffs associated with mapping to various architectures
  • Addresses a range of fundamental programming techniques, with multiple examples and case studies that demonstrate OpenCL extensions for a variety of hardware platforms
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Editorial Reviews

From the Publisher
With parallel computing now in the mainstream, this book provides an excellent reference on the state-of-the-art techniques in accelerating applications on CPU-GPU systems.

-David A. Bader, Georgia Institute of Technology

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Product Details

  • ISBN-13: 9780124055209
  • Publisher: Elsevier Science
  • Publication date: 12/31/2012
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 2
  • Pages: 308
  • Sales rank: 1,181,936
  • File size: 6 MB

Meet the Author

Benedict R. Gaster is a software architect working on programming models for next-generation heterogeneous processors, in particular looking at high-level abstractions for parallel programming on the emerging class of processors that contain both CPUs and accelerators such as GPUs. Benedict has contributed extensively to the OpenCL's design and has represented AMD at the Khronos Group open standard consortium. Benedict has a Ph.D in computer science for his work on type systems for extensible records and variants.

Lee Howes has spent the last two years working at AMD and currently focuses on programming models for the future of heterogeneous computing. Lee's interests lie in declaratively representing mappings of iteration domains to data and in communicating complicated architectural concepts and optimizations succinctly to a developer audience, both through programming model improvements and education. Lee has a Ph.D. in computer science from Imperial College London for work in this area.

David Kaeli received a BS and PhD in Electrical Engineering from Rutgers University, and an MS in Computer Engineering from Syracuse University. He is the Associate Dean of Undergraduate Programs in the College of Engineering and a Full Processor on the ECE faculty at Northeastern University, Boston, MA where he directs the Northeastern University Computer Architecture Research Laboratory (NUCAR). Prior to joining Northeastern in 1993, Kaeli spent 12 years at IBM, the last 7 at T.J. Watson Research Center, Yorktown Heights, NY.

Dr. Kaeli has co-authored more than 200 critically reviewed publications. His research spans a range of areas including microarchitecture to back-end compilers and software engineering. He leads a number of research projects in the area of GPU Computing. He presently serves as the Chair of the IEEE Technical Committee on Computer Architecture. Dr. Kaeli is an IEEE Fellow and a member of the ACM.

Perhaad Mistry is a PhD candidate at Northeastern University. He received a BS in Electronics Engineering from University of Mumbai and an MS in Computer Engineering from Northeastern University in Boston. He is presently a member of the Northeastern University Computer Architecture Research Laboratory (NUCAR) and is advised by Dr. David Kaeli.

Perhaad works on a variety of parallel computing projects. He has designed scalable data structures for the physics simulations for GPGPU platforms and has also implemented medical reconstruction algorithms for heterogeneous devices. His present research focuses on the design of profiling tools for heterogeneous computing, He is studying the potential of using standards like OpenCL for building tools that simplify parallel programming and performance analysis across the variety of heterogeneous devices available today.

Dana Schaa received a BS in Computer Engineering from Cal Poly, San Luis Obispo, and an MS in Electrical and Computer Engineering from Northeastern University, where he is also currently a Ph. D. candidate. His research interests include parallel programming models and abstractions, particularly for GPU architectures. He has developed GPU-based implementations of several medical imaging research projects ranging from real-time visualization to image reconstruction in distributed, heterogeneous environments. Dana married his wonderful wife Jenny in 2010, and they live together in Boston with their charming cats.

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Table of Contents

  1. Introduction to Parallel Programming
  2. Introduction to OpenCL
  3. OpenCL Device Architectures
  4. Basic OpenCL Examples
  5. Understanding OpenCL's Concurrency and Execution Model
  6. Dissecting a CPU/GPU OpenCL Implementation
  7. Data Management
  8. OpenCL Case Study: Convolution
  9. OpenCL Case Study: Histogram
  10. OpenCL Case Study: Mixed Particle Simulation
  11. OpenCL Extensions
  12. Foreign Lands: Plugging OpenCL In
  13. OpenCL Profiling and Debugging
  14. Performance Optimization of an Image Analysis Application
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  • Anonymous

    Posted May 19, 2014

    Mandi

    She is in her closet naked. Which outfit?

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