Chapter 1, Using OpenCL, sets the stage for the reader by establishing OpenCL in its purpose and motivation. The core concepts are outlined in the recipes covering the intrinsics of devices and their interactions and also by real working code. The reader will learn about contexts and devices and how to create code that runs on those devices.

Chapter 2, Understanding OpenCL Data Transfer and Partitioning, discusses the buffer objects in OpenCL and strategies on how to partition data amongst them. Subsequently, readers will learn what work items are and how data partitioning can take effect by leveraging OpenCL abstractions.

Chapter 3, Understanding OpenCL Data Types, explains the two general data types that OpenCL offers, namely scalar and vector data types, how they are used to solve different problems, and how OpenCL abstracts native vector architectures in processors. Readers will be shown how they can effect programmable vectorization through OpenCL.

Chapter 4, Understanding OpenCL Functions, discusses the various functionalities offered by OpenCL in solving day-to-day problems, for example, geometry, permuting, and trigonometry. It also explains how to accelerate that by using their vectorized counterparts.

Chapter 5, Developing a Histogram OpenCL program, witnesses the lifecycle of a typical OpenCL development. It also discusses about the data partitioning strategies that rely on being cognizant of the algorithm in question. The readers will inadvertently realize that not all algorithms or problems require the same approach.

Chapter 6, Developing a Sobel Edge Detection Filter, will guide you in how to build an edge detection filter using the Sobel's method. They will be introduced into some mathematical formality including convolution theory in one-dimension and two-dimensions and its accompanying code. And finally, we introduce how profiling works in OpenCL and its application in this recipe.

Chapter 7, Developing the Matrix Multiplication with OpenCL, discusses parallelizing the matrix multiplication by studying its parallel form and applying the tranformation from sequential to parallel. Next, it'll optimize the matrix multiplication by discussing how to increase the computation throughput and warming the cache.

Chapter 8, Developing the Sparse Matrix-Vector Multiplication with OpenCL, discusses the context of this computation and the conventional method used to solve it, that is, the conjugate gradient through enough math. Once that intuition is developed, readers will be shown how various storage formats for sparse matrices can affect the parallel computation and then the readers can examine the ELLPACK, ELLPACK-R, COO, and CSR.

Chapter 9, Developing Bitonic Sort Using OpenCL, will introduce readers, to the world of sorting algorithms, and focus on the parallel sorting network also known as bitonic sort. This chapter works through the recipes, as we did in all other chapters by presenting the theory and its sequential implementation, and extracting the parallelism from the transformation, and then developing the final parallel version.

Chapter 10, Developing the Radix Sort with OpenCL, will introduce a classic example of non-comparison based sorting algorithms, for example, QuickSort where it suits a GPU architecture better. The reader is also introduced to another core parallel programming technique known as reduction, and we developed the intuition of how reduction helps radix sort perform better. The radix sort recipe also demonstrates multiple kernel programming and highlights the advantages as well as the disadvantages.

You need to be comfortable working in a Linux environment, as the examples are tested against the Ubuntu 12.10 64-bit operating system. The following are the requirements:

This book is intended for software developers who have often wondered what to do with that newly bought CPU or GPU they bought other than using it for playing computer games. Having said that, this book isn't about toy algorithms that works only on your workstations at home. This book is ideally for the developers who have a working knowledge of C/C++ and who want to learn how to write parallel programs that execute in heterogeneous computing environments in OpenCL.

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text are shown as follows: "We can include other contexts through the use of the #include directive."

A block of code is set as follows:

[default]
cl_uint sortOrder = 0; // descending order else 1 for ascending order
        cl_uint stages = 0;
        for(unsigned int i = LENGTH; i > 1; i >>= 1)
            ++stages;
        clSetKernelArg(kernel, 0, sizeof(cl_mem),(void*)&device_A_in);
        clSetKernelArg(kernel, 3, sizeof(cl_uint),(void*)&sortOrder);
#ifdef USE_SHARED_MEM
        clSetKernelArg(kernel, 4, (GROUP_SIZE << 1) *sizeof(cl_uint),NULL);
#elif def USE_SHARED_MEM_2

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

[default]
cl_uint sortOrder = 0; // descending order else 1 for ascending order
        cl_uint stages = 0;
        for(unsigned int i = LENGTH; i > 1; i >>= 1)
            ++stages;
        clSetKernelArg(kernel, 0, sizeof(cl_mem),(void*)&device_A_in);
        clSetKernelArg(kernel, 3, sizeof(cl_uint),(void*)&sortOrder);
#ifdef USE_SHARED_MEM
        clSetKernelArg(kernel, 4, (GROUP_SIZE << 1) *sizeof(cl_uint),NULL);
#elif def USE_SHARED_MEM_2

Any command-line input or output is written as follows:

# gcc –Wall test.c –o test

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "clicking on the Next button moves you to the next screen".

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