OpenCL异构计算编程指南

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"Heterogeneous Computing with OpenCL Revised OpenCL 1.2 Edition" 本书《异构计算与 OpenCL》详细介绍了 OpenCL 编程的所有方面，帮助程序员快速掌握 OpenCL 编程技术。书中涵盖了 OpenCL 的基本概念、编程步骤、代码示例等内容，为读者提供了一个系统的学习 OpenCL 的平台。 OpenCL 是一种异构计算平台，可以在多种硬件平台上运行，包括 CPU、GPU、FPGA 等。OpenCL 的出现使得程序员可以轻松地开发跨平台的并行计算应用程序。书中详细介绍了 OpenCL 的架构、编程模型、内存模型、执行模型等方面的内容，为读者提供了一个深入理解 OpenCL 的机会。书中还涵盖了 OpenCL 的实践应用，包括图像处理、数据挖掘、机器学习等领域的实践案例，为读者提供了一个学习 OpenCL 的实践平台。《异构计算与 OpenCL》是一本详细、系统、实践的 OpenCL 编程指南，适合那些想学习 OpenCL 的程序员和开发者。知识点： 1. 异构计算的概念和架构 2. OpenCL 的基本概念和编程模型 3. OpenCL 的内存模型和执行模型 4. OpenCL 的实践应用：图像处理、数据挖掘、机器学习等 5. OpenCL 和 CUDA 的比较 6. OpenCL 在 GPU 硬件平台上的应用 7. OpenCL 的跨平台支持 8. OpenCL 的编程步骤和代码示例本书适合以下读者： 1. 想要学习 OpenCL 编程的程序员和开发者 2. 需要使用 OpenCL 开发跨平台并行计算应用程序的开发者 3. 感兴趣于学习异构计算和 OpenCL 的研究人员和学生《异构计算与 OpenCL》是一本非常有价值的参考书籍，适合那些想学习 OpenCL 编程和异构计算的读者。

Dana Schaa received a B.S. in computer engineering from California Polytechnic

State University, San Luis Obispo, and an M.S. in electrical and computer engineer-

ing from Northeastern University, where he is also currently a Ph.D. candidate.

His research interests include parallel programming models and abstractions,

particularly for GPU architectures. He has developed GPU-based implementations

of several medical imaging researc h projects ranging from real-time visualization

to image reconstruction in distributed, heterogeneous environments. He married

his wonderful wife, Jenny, in 2010, and they live together in Boston with their

charming cats.

xvAbout the authors

CHAPTER

Introduction to Parallel

Programming

INTRODUCTION

Today’s computing environments are becoming more multifaceted, exploiting the

capabilities of a range of multi-core microprocessors, central processing units

(CPUs), digital signal processors, reconfigurable hardware (FPGAs), and graphics

processing units (GPUs). Presented with so much heterogeneity, the process of de-

veloping efficient software for such a wide array of architectures poses a number of

challenges to the programming community.

Applications possess a number of workload behaviors, ranging from control

intensive (e.g., searching, sorting, and parsing) to data intensive (e.g., image

processing, simulation and modeling, and data mining). Applications can also

be characterized as compute intensive (e.g., iterative methods, numerical methods,

and financial modeling), where the overall throughput of the application is heavily

dependent on the computational efficiency of the underlying hardware. Each of

these workload classes typically executes most efficiently on a specific style of

hardware architecture. No single architecture is best for running all classes of

workloads, and most applications possess a mix of the workload characteristics.

For instance, control-intensive applications tend to run faster on superscalar CPUs,

where significant die real estate has been devoted to branch prediction mecha-

nisms, whereas data-intensive applications tend to run fast on vector architectures,

where the same operation is applied to multiple data items concurrently.

OPENCL

The Open Computing Language (OpenCL) is a heteroge neous programming

framework that is managed by the nonprofit technology consortium Khronos

Group. OpenCL is a framework for developing applications that execute across

a range of device types made by different vendors. It supports a wide range of

levels of parallelism and efficiently maps to homogeneous or heterogeneous,

single- or multiple-device systems consisting of CPUs, GPUs, and other types of de-

vices limited only by the imagination of vendors. The OpenCL definition offers both

a device-side lang uage and a host management layer for the devices in a system.

Heterogeneous Computing with OpenCL

The device-side language is designed to efficiently map to a wide range of memory

systems. The host language aims to support efficient plumbi ng of complicated concur-

rent programs with low overhead. Together, these provide the developer with a path to

efficiently move from algorithm design to implementation.

OpenCL provides parallel computing using task-based and data-based parallel-

ism. It currently supports CPUs that include x86, ARM, and PowerPC, and it has

been adopted into graphics card drivers by AMD, Apple, Intel, and NVIDIA. Support

for OpenCL is rapidly expanding as a wide range of platform vendors have adopted

OpenCL and support or plan to support it for their hardware platforms. These vendor s

fall within a wide range of market segments, from the embedded vendors (ARM and

Imagination Technologies) to the HPC vendors (AMD, Intel, NVIDIA, and IBM).

The architectures supported include multi-core CPUs, thr oughput and vector proces-

sors such as GPUs, and fine-grained parallel devices such as FPGAs.

Most important, OpenCL’s cross-platform, industrywide support makes it an

excellent programming model for developers to learn and use, with the confidence

that it will continue to be widely available for years to come with ever-increasing

scope and applicability.

THE GOALS OF THIS BOOK

The first edition of this book was the first of its kind to present OpenCL programming

in a fashion appropriate for the classroom. In this second edition we update the con-

tent for the latest version of the OpenCL standard. The book is organized to address

the need for teaching parallel programming on current system architectures using

OpenCL as the target language, and it includes examples for CPUs, GPUs, and their

integration in the accelerated processing unit (APU). Another major goal of this text

is to provide a guide to programmers to develop well-designed programs in OpenCL

targeting parallel systems. The book leads the programmer through the various ab-

stractions and features provided by the OpenCL programming environment. The ex-

amples offer the reader a simple introduction and more complicated optimizations,

and they suggest further development and goals at which to aim. It also discusses

tools for improving the development process in terms of profiling and debugging

such that the reader need not feel lost in the development process.

The book is accompanied by a set of instructor slides and program ming exam-

ples, which support the use of this text by an OpenCL instructor. Please visit

http://heterogeneouscomputingwithopencl.org/ for additional information.

THINKING PARALLEL

Most applications are first programmed to run on a single processor. In the field

of high-performance computing, classical approaches have been used to accelerate

computation when provided with multiple computing resources. Standard approaches

2 CHAPTER 1 Introduction to parallel programming

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