OpenCL异构计算实战指南

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"OpenCL异构计算 - 一本关于使用OpenCL进行异构计算的开发教程，由Benedict Gaster、Lee Howes、David R. Kaeli、Perhaad Mistry和Dana Schaa合作撰写。" OpenCL（Open Computing Language）是一种开放标准，用于实现跨平台的并行编程，特别是针对异构计算环境设计。异构计算是指在一个系统中结合使用不同类型的处理器，如CPU（中央处理器）、GPU（图形处理器）、FPGA（现场可编程门阵列）或加速器等，以提高计算效率和性能。OpenCL提供了一个统一的编程模型，使得开发者能够利用这些硬件的全部潜力。本书"OpenCL异构计算"涵盖了以下关键知识点： 1. **基础概念**：首先，读者将了解OpenCL的基本原理，包括工作项、工作组、计算设备和上下文的概念。这些是构建OpenCL程序的基础，用于管理和调度计算任务。 2. **OpenCL架构**：深入探讨OpenCL架构，包括主机端代码和设备端代码（内核）的分离，以及如何通过OpenCL API与设备进行通信。 3. **数据并行性**：OpenCL支持大规模的数据并行处理，允许同时处理大量独立的数据元素。书中的例子会展示如何设计和优化并行算法以充分利用硬件资源。 4. **内存模型**：理解OpenCL的内存层次结构，如全局内存、局部内存、私有内存和常量内存，对于编写高效代码至关重要。书中会解释如何有效地管理这些内存区域。 5. **计算内核**：学习编写OpenCL内核函数，这些函数将在目标设备上执行。内核的设计和优化是提升性能的关键。 6. **同步与通信**：掌握在OpenCL中进行设备间通信和同步的方法，包括屏障、事件和队列的使用。 7. **平台和设备选择**：了解如何根据应用需求选择合适的计算平台和设备，并配置OpenCL环境。 8. **性能分析和调优**：书中会介绍工具和技术来分析OpenCL程序的性能，以及如何进行性能调优，以获得最佳运行速度。 9. **实用示例**：通过实际案例，比如图像处理、物理模拟或机器学习，来展示OpenCL在异构计算中的应用。 10. **最佳实践**：学习OpenCL编程的最佳实践，以避免常见的陷阱和错误，提高代码的可移植性和可维护性。此书适合对并行计算感兴趣的开发者，无论是在学术研究还是在工业应用中，都能从中获取到实现高性能计算所需的技能和知识。通过学习OpenCL，开发者能够编写出能够在各种硬件平台上运行的高效并行程序，从而充分利用现代计算系统的强大能力。

About the Authors

Benedict R. Gaster is a software architect working on programming models for

next-generation heterogeneous processors, particularly examining high-level ab-

stractions for para llel programming on the emerging class of processors that contain

both CPUs and accelerators such as GPUs. He has contributed extensively to the

OpenCL’s design and has represented AMD at the Khronos Group open standard

consortium. He has a Ph.D. in computer science for his work on type systems for

extensible records and variants.

Lee Howes has spent the past 2 years working at AMD and currently focuses on

programming models for the future of heterogeneous computing. His interests lie

in declaratively representing mappings of iteration domains to data and in commu-

nicating complicated architectural concepts and optimizations succinctly to a devel-

oper audience, both through programming model improvements and through

education. He has a Ph.D. in computer science from Imperial College London for

work in this area.

David Kaeli received a B.S. and Ph.D. in electrical engineering from Rutgers Uni-

versity and an M.S. in computer engineering from Syracuse University. He is Asso-

ciate Dean of Undergraduate Programs in the Coll ege of Engineering and a Full

Professor on the ECE faculty at Northeastern University, where he directs the North-

eastern University Computer Architecture Research Laboratory (NUCAR). Prior to

joining Northeastern in 1993, he spent 12 year s at IBM, the last 7 at T. J. Watson

Research Center, Yorktown Heights, NY. He has co-authored more than 200 criti-

cally reviewed publications. His rese arch spans a range of areas, including micro-

architecture to back-end compilers and software engineering. He leads a number

of research projects in the area of GPU computing. He currently serves as the Chair

of the IEEE Technical Committee on Computer Architecture. He is an IEEE Fellow

and a member of the ACM.

Perhaad Mistry is a Ph.D. candidate at Northeastern University. He received a B.S.

in electronics engineering from the University of Mumbai and an M.S. in computer

engineering from Northeastern University. He is currently a membe r of the North-

eastern University Computer Architecture Research Laboratory (NUCAR) and is ad-

vised by Dr. David Kaeli. He works on a variety of parallel computing projects. He

has designed scalable data structures for the physics simulations for GPGPU plat-

forms and has also implemented medical reconstruction algorithms for heteroge-

neous devices. His current research focuses on the design of profiling tools for

heterogeneous computing. He is studying the potential of using standards such as

OpenCL for building tools that simplify parallel programming and performance

analysis across the variety of heterogeneous devices available today.

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 graphic

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

intensiv

e (e.g., searchi

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

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

be characterized as com pute 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 heterogeneous 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 language 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 plumbing 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 both AMD (called the Accelerated

Parallel Processing SDK) 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 vendors 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, throughput and vector processors 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

This book is the first of its kind to present OpenCL programming in a fashion appro-

priate for the classroom. The book is organized to address the need for teaching par-

allel programming on current system architectures using OpenCL as the target

language, and it includes examples for CPUs, GPUs, and their integration in the ac-

celerated 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 abstractions and fea-

tures provided by the OpenCL programming environment. The examples offer the

reader a simple introduction and more complicated opti mizations, 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 programming 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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