Heterogeneous Computing with OpenCL 2. 0 |
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| Author:
| Kaeli, David R.
Mistry, Perhaad
Schaa, Dana
Zhang, Dong Ping |
| ISBN: | 978-0-12-801649-7 |
| Publication Date: | Jun 2015 |
| Publisher: | Elsevier Science & Technology Books
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| Imprint: | Morgan Kaufmann |
| Book Format: | Ebook |
| List Price: | USD $74.95USD $89.94 |
Book Description:
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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...
More Description
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.
- 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