By Benedict Gaster, Lee Howes, David R. Kaeli, Perhaad Mistry, Dana Schaa
Heterogeneous Computing with OpenCL teaches OpenCL and parallel programming for complicated structures that can contain various equipment architectures: multi-core CPUs, GPUs, and fully-integrated sped up Processing devices (APUs) equivalent to AMD Fusion expertise. Designed to paintings on a number of structures and with extensive help, OpenCL can assist you extra successfully software for a heterogeneous future.
Written by way of leaders within the parallel computing and OpenCL groups, this ebook offers you hands-on OpenCL adventure to deal with a variety of primary parallel algorithms. The authors discover reminiscence areas, optimization thoughts, portraits interoperability, extensions, and debugging and profiling. meant to aid a parallel programming path, Heterogeneous Computing with OpenCL contains unique examples all through, plus extra on-line workouts and different aiding materials.
- Explains ideas and techniques to benefit parallel programming with OpenCL, from knowing the 4 abstraction versions to completely checking out and debugging whole applications.
- Covers photograph processing, net plugins, particle simulations, video modifying, functionality optimization, and more.
- Shows how OpenCL maps to an instance objective structure and explains a few of the tradeoffs linked to mapping to varied architectures
- Addresses various basic programming concepts, with a number of examples and case stories that exhibit OpenCL extensions for various platforms
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Additional resources for Heterogeneous Computing with OpenCL
Whenever data is transferred from the host to the device, the data will reside in global memory. Any data that is to be transferred back from the device to the host must also reside in global memory. The keyword global is added to a pointer declaration to specify that data referenced by the pointer resides in global memory. For example, in the OpenCL C code at the end of the chapter global float* A, the data pointed to by A resides in global memory (although we will see that A actually resides in private memory).
Samplers specify how out-of-bounds image accesses are handled, whether interpolation should be used, and if coordinates are normalized. , storing in the proper channel and with the proper size). Chapter 4 provides an example of an OpenCL program that uses images. Flush and Finish The flush and finish commands are two different types of barrier operations for a command queue. The clFinish() function blocks until all of the commands in a command queue have completed; its functionality is synonymous with a synchronization barrier.
However, as the number of platforms supporting OpenCL grows (particularly in the embedded systems space), the overall impact of OpenCL should increase substantially. Heterogeneous Computing with OpenCL © 2012 Advanced Micro Devices, Inc. Published by Elsevier Inc. All rights reserved. ” Is this any more than the graphics-specific logic that the device still carries? More questions arise when we try to think about this question in any detail. How many cores does a GPU have? ” What is a “many-core” device, and is it significantly different from a “multi-core” device?