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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

OpenCL implementation of filters


Here we discuss how each of the filters is implemented. Similar filters are discussed together. Mean and Gaussian filters are imposed by convoluting with a fixed 3 x 3 matrix.

Mean and Gaussian filter

In our OpenCL implementation of the Mean and Gaussian filters, we write a kernel called filter_kernel that can be used for the two filters. We do this by configuring the third argument filter so that it can create effects of the corresponding filter. For the Mean filter we send a nine element array, where each element's value is 1/9 and when this is convoluted with the corresponding window, it produces the effect of mean of that window. When the filter_kernel kernel is to be called for the Gaussian filter, we pass corresponding coefficients in row major form (1/16, 2/16, 1/16, 2/16, 4/16, 2/16, 1/16, 2/16, 1/16).

So we explain these two filters together. The following code is the common kernel code for these two filters:

__constant sampler_t image_sampler = CLK_NORMALIZED_COORDS_FALSE...
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OpenCL Programming by Example
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