OpenCL Parallel Programming Development Cookbook

More Information
Learn
  • How to use OpenCL
  • Understand data partitioning and transfers in OpenCL
  • Understand OpenCL data types
  • Learn about OpenCL functions including math, atomic, threading model, data transfer, and so on
  • Develop a histogram in OpenCL
  • Learn how to develop Sobel edge detection in OpenCL for image processing
  • Develop the Matrix Multiplication and the Sparse Matrix Vector Multiplication in OpenCL
  • Learn to develop Bitonic sort and Radix sort in OpenCL
About

OpenCL (Open Computing Language) is the first royalty-free standard for cross platform, parallel programming of modern processors found in personal computers, servers, mobiles, and embedded devices. OpenCL greatly improves speed and responsiveness for a wide spectrum of applications in numerous market categories, from gaming and entertainment to scientific and medical software. OpenCL has proved itself to be versatile in that it now runs on not only operating systems like Windows and Linux powered by Intel and AMD processors, but also on low power chips like ARM, and it has also been adopted by processor manufacturers like ARM Corp, Vivante, and Altera, among others.

OpenCL Parallel Programming Development Cookbook was designed to be practical so that we achieve a good balance between theory and application. Learning to program in a parallel way is relatively easy, but to be able to take advantage of all of the resources available to you efficiently is quite different. You need to be shown not only application, but also the theory behind it.

This book is roughly in two parts, where the first part is the fundamentals of OpenCL parallel development and the second part is the various algorithms we will explore with you. Each part is packed with many code samples and illustrations to demonstrate various concepts. The first part is essential for a beginner to not only program in parallel, but also to think in parallel and become equipped with the mental model with which to tackle parallel programming. The second part consists of seven different algorithms that the author has identified; you will learn various parallel programming techniques that experts have used in the past 60 years that are applicable to OpenCL.

This book will demonstrate how you think in parallel by illustrating and demonstrating programming techniques like data partitioning, thread coarsening, register tiling, data pre-fetching, and algorithm transformation. These techniques are demonstrated in the seven algorithms you’ll be shown, from image processing and solving sparse linear systems to in-memory sorting.
OpenCL Parallel Programming Development Cookbook combines recipes, illustrations, code, and explanations to allow you to learn the essentials of parallel programming in OpenCL, and the author has added in enough math so that the readers understand the motivation and can also lay the foundation upon which they will begin their own exploration.

Features
  • Learn about parallel programming development in OpenCL and also the various techniques involved in writing high-performing code
  • Find out more about data-parallel or task-parallel development and also about the combination of both
  • Understand and exploit the underlying hardware features like processor registers and caches that run potentially tens of thousands of threads across the processors
Page Count 302
Course Length 9 hours 3 minutes
ISBN 9781849694520
Date Of Publication 25 Aug 2013

Authors

Raymond Tay

Raymond Tay has been a software developer for the past decade and his favorite programming languages include Scala, Haskell, C, and C++. He started playing with GPGPU technology since 2008, first with the CUDA toolkit by NVIDIA and OpenCL toolkit by AMD, and then Intel. In 2009, he decided to submit a GPGPU project on which he was working to the editorial committee working on the "GPU Computing Gems" to be published by Morgan Kauffmann. And though his work didn't make it to the final published work, he was very happy to have been short-listed for candidacy. Since then, he's worked on projects that use GPGPU technology and techniques in CUDA and OpenCL. He's also passionate about functional programming paradigms and their applications in cloud computing which has led him investigating on various paths to accelerate applications in the cloud through the use of GPGPU technology and the functional programming paradigm. He is a strong believer of continuous learning and hopes to be able to continue to do so for as long as he possibly can.