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OpenCL Programming by Example

OpenCL Programming by Example

By : Banger, Koushik Bhattacharyya
3.3 (7)
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OpenCL Programming by Example

OpenCL Programming by Example

3.3 (7)
By: Banger, Koushik Bhattacharyya

Overview of this book

Research in parallel programming has been a mainstream topic for a decade, and will continue to be so for many decades to come. Many parallel programming standards and frameworks exist, but only take into account one type of hardware architecture. Today computing platforms come with many heterogeneous devices. OpenCL provides royalty free standard to program heterogeneous hardware. This guide offers you a compact coverage of all the major topics of OpenCL programming. It explains optimization techniques and strategies in-depth, using illustrative examples and also provides case studies from diverse fields. Beginners and advanced application developers will find this book very useful. Beginning with the discussion of the OpenCL models, this book explores their architectural view, programming interfaces and primitives. It slowly demystifies the process of identifying the data and task parallelism in diverse algorithms. It presents examples from different domains to show how the problems within different domains can be solved more efficiently using OpenCL. You will learn about parallel sorting, histogram generation, JPEG compression, linear and parabolic regression and k-nearest neighborhood, a clustering algorithm in pattern recognition. Following on from this, optimization strategies are explained with matrix multiplication examples. You will also learn how to do an interoperation of OpenGL and OpenCL. "OpenCL Programming by Example" explains OpenCL in the simplest possible language, which beginners will find it easy to understand. Developers and programmers from different domains who want to achieve acceleration for their applications will find this book very useful.
Table of Contents (13 chapters)
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12
Index

General tips


Some of the following strategies are vendor and architecture specific but mostly have a corresponding counterpart in other vendors and architectures.

  1. Try to minimize host-device transfer of memory. Also try to hide memory transfer latencies with parallel computation. Host-device transfer has much lower bandwidth than global memory access. (For example, for NVIDIA GTX 280 verses PCI-e it becomes approximately 17 times). So better to store and keep it on the Global memory. Sometimes it is even better to re-compute something in GPU rather than trying to fetch from host.

  2. One large transfer is much better than many smaller transfers amounting to same size.

  3. Try for coalesced memory access as much as possible, that is, avoid out of sequence and misaligned transactions. This is more OpenCL device architecture and compute capability specific.

  4. Use local memory (100 times better latency for GTX 280) for caching, but be careful about overuse to avoid performance penalty due to spilling to global...

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