Book Image

OpenCL Programming by Example

Book Image

OpenCL Programming by Example

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 (18 chapters)
OpenCL Programming by Example
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Finding the scope of the use of OpenCL


Given an algorithm or even some sequential implementation of it, how do we determine whether OpenCL would really help in performance gain? First, find hotspots in your sequential code. If that hot part can be partitioned into smaller parts which can be executed at least to some extent independently, that is, one smaller computation part can be done without waiting for data of previous computation part? Can we find some part of the algorithm where the same instruction is executed on different data without any mutual dependency? Affirmative answer to the first and second part of questions respectively asserts the existence of task and data parallel components in the algorithm. In either case, taking advantage of OpenCL is probably a good option.

Second consideration is whether the program is memory-bound, I/O bound, or CPU-bound. If the algorithm is dense with conditional statements, a better candidate for OpenCL would be compute bound program with less...