Preface

This book is designed as a concise introduction to OpenCL programming for developers working on diverse domains. It covers all the major topics of OpenCL programming and illustrates them with code examples and explanations from different fields such as common algorithm, image processing, statistical computation, and machine learning. It also dedicates one chapter to Optimization techniques, where it discusses different optimization strategies on a single simple problem.

Parallel programming is a fast developing field today. As it is becoming increasingly difficult to increase the performance of a single core machine, hardware vendors see advantage in packing multiple cores in a single SOC. The GPU (Graphics Processor Unit) was initially meant for rendering better graphics which ultimately means fast floating point operation for computing pixel values. GPGPU (General purpose Graphics Processor Unit) is the technique of utilization of GPU for a general purpose computation. Since the GPU provides very high performance of floating point operations and data parallel computation, it is very well suited to be used as a co-processor in a computing system for doing data parallel tasks with high arithmetic intensity.

Before NVIDIA^® came up with CUDA (Compute Unified Device Architecture) in February 2007, the typical GPGPU approach was to convert general problems' data parallel computation into some form of a graphics problem which is expressible by graphics programming APIs for the GPU. CUDA first gave a user friendly small extension of C language to write code for the GPU. But it was a proprietary framework from NVIDIA and was supposed to work on NVIDIA's GPU only.

With the growing popularity of such a framework, the requirement for an open standard architecture that would be able to support different kinds of devices from various vendors was becoming strongly perceivable. In June 2008, the Khronos compute working group was formed and they published OpenCL1.0 specification in December 2008. Multiple vendors gradually provided a tool-chain for OpenCL programming including NVIDIA OpenCL Drivers and Tools, AMD APP SDK, Intel^® SDK for OpenCL application, IBM Server with OpenCL development Kit, and so on. Today OpenCL supports multi-core programming, GPU programming, cell and DSP processor programming, and so on.

In this book we discuss OpenCL with a few examples.