计算机体系结构：量化研究方法第五版——解析GPU架构

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"计算机体系结构-量化研究方法第五版" 《计算机体系结构-量化研究方法》第五版是计算机科学领域的一本经典教材，由John L. Hennessy和David A. Patterson两位著名学者撰写。这本书深入浅出地介绍了当前计算平台的最新知识，并提供了架构洞见，帮助学生和专业人士设计未来的系统。第五版特别更新了关于数据级并行性的章节，以传统计算机架构术语解释GPU架构，消除了其复杂性。计算机体系结构是计算机科学的核心部分，它涉及如何设计、组织和实现计算机硬件和软件之间的交互。本书强调了量化方法，即通过数据和度量来理解和评估计算机性能，这对于理解现代计算机系统的复杂性至关重要。量化研究方法让读者能够基于实际性能指标来比较和优化不同的设计方案。第五版的更新内容包括对整个文本的全面修订，确保了信息的时效性和准确性。特别是第六章，针对数据级并行性的深度探讨，揭示了GPU（图形处理单元）在高性能计算中的作用。GPU最初被设计用于图形渲染，但近年来已经成为通用计算的重要工具，尤其是在机器学习和深度学习领域。此外，书中还涵盖了其他关键主题，如处理器设计（包括单核和多核）、存储层次结构、内存系统、I/O子系统和能量效率。这些主题不仅涵盖了传统的桌面和服务器系统，还涉及嵌入式系统、移动设备以及云计算环境中的计算架构。 Hennessy和Patterson的写作风格清晰易懂，他们将复杂的概念分解成可管理的部分，使读者能够逐步理解并应用到实际设计中。本书不仅适合计算机科学的学生，也是研究人员和工程师的重要参考资料，帮助他们在快速发展的计算领域保持与时俱进。《计算机体系结构-量化研究方法》第五版是一本全面且权威的指南，它提供了一个深入理解计算机体系结构的框架，使读者能够掌握设计高效、节能计算系统的必要技能。无论是在学术界还是工业界，这本书都是不可或缺的资源，对于想要在计算机体系结构领域深化知识的人来说，是必备的读物。

Why We Wrote This Book

Through five editions of this book, our goal has been to describe the basic princi-

ples underlying what will be tomorrow’s technological developments. Our excite-

ment about the opportunities in computer architecture has not abated, and we

echo what we said about the field in the first edition: “It is not a dreary science of

paper machines that will never work. No! It’s a discipline of keen intellectual

interest, requiring the balance of marketplace forces to cost-performance-power,

leading to glorious failures and some notable successes.”

Our primary objective in writing our first book was to change the way people

learn and think about computer architecture. We feel this goal is still valid and

important. The field is changing daily and must be studied with real examples

and measurements on real computers, rather than simply as a collection of defini-

tions and designs that will never need to be realized. We offer an enthusiastic

welcome to anyone who came along with us in the past, as well as to those who

are joining us now. Either way, we can promise the same quantitative approach

to, and analysis of, real systems.

As with earlier versions, we have strived to produce a new edition that will

continue to be as relevant for professional engineers and architects as it is for

those involved in advanced computer architecture and design courses. Like the

first edition, this edition has a sharp focus on new platforms—personal mobile

devices and warehouse-scale computers—and new architectures—multicore and

GPUs. As much as its predecessors, this edition aims to demystify computer

architecture through an emphasis on cost-performance-energy trade-offs and

good engineering design. We believe that the field has continued to mature and

move toward the rigorous quantitative foundation of long-established scientific

and engineering disciplines.

Preface

xvi

■

Preface

This Edition

We said the fourth edition of Computer Architecture: A Quantitative Approach

may have been the most significant since the first edition due to the switch to

multicore chips. The feedback we received this time was that the book had lost

the sharp focus of the first edition, covering everthing equally but without empha-

sis and context. We’re pretty sure that won’t be said about the fifth edition.

We believe most of the excitement is at the extremes in size of computing,

with personal mobile devices (PMDs) such as cell phones and tablets as the cli-

ents and warehouse-scale computers offering cloud computing as the server.

(Observant readers may seen the hint for cloud computing on the cover.) We are

struck by the common theme of these two extremes in cost, performance, and

energy efficiency despite their difference in size. As a result, the running context

through each chapter is computing for PMDs and for warehouse scale computers,

and Chapter 6 is a brand-new chapter on the latter topic.

The other theme is parallelism in all its forms. We first idetify the two types of

application-level parallelism in Chapter 1: data-level parallelism (DLP), which

arises because there are many data items that can be operated on at the same time,

and task-level parallelism (TLP), which arises because tasks of work are created

that can operate independently and largely in parallel. We then explain the four

architectural styles that exploit DLP and TLP: instruction-level parallelism (ILP)

in Chapter 3; vector architectures and graphic processor units (GPUs) in Chapter

4, which is a brand-new chapter for this edition; thread-level parallelism in

Chapter 5; and request-level parallelism (RLP) via warehouse-scale computers in

Chapter 6, which is also a brand-new chapter for this edition. We moved memory

hierarchy earlier in the book to Chapter 2, and we moved the storage systems

chapter to Appendix D. We are particularly proud about Chapter 4, which con-

tains the most detailed and clearest explanation of GPUs yet, and Chapter 6,

which is the first publication of the most recent details of a Google Warehouse-

scale computer.

As before, the first three appendices in the book give basics on the MIPS

instruction set, memory hierachy, and pipelining for readers who have not read a

book like Computer Organization and Design. To keep costs down but still sup-

ply supplemental material that are of interest to some readers, available online at

http://booksite.mkp.com/9780123838728/ are nine more appendices. There are

more pages in these appendices than there are in this book!

This edition continues the tradition of using real-world examples to demon-

strate the ideas, and the “Putting It All Together” sections are brand new. The

“Putting It All Together” sections of this edition include the pipeline organiza-

tions and memory hierarchies of the ARM Cortex A8 processor, the Intel core i7

processor, the NVIDIA GTX-280 and GTX-480 GPUs, and one of the Google

warehouse-scale computers.

Preface

■

xvii

Topic Selection and Organization

As before, we have taken a conservative approach to topic selection, for there are

many more interesting ideas in the field than can reasonably be covered in a treat-

ment of basic principles. We have steered away from a comprehensive survey of

every architecture a reader might encounter. Instead, our presentation focuses on

core concepts likely to be found in any new machine. The key criterion remains

that of selecting ideas that have been examined and utilized successfully enough

to permit their discussion in quantitative terms.

Our intent has always been to focus on material that is not available in equiva-

lent form from other sources, so we continue to emphasize advanced content

wherever possible. Indeed, there are several systems here whose descriptions

cannot be found in the literature. (Readers interested strictly in a more basic

introduction to computer architecture should read Computer Organization and

Design: The Hardware/Software Interface.)

An Overview of the Content

Chapter 1 has been beefed up in this edition. It includes formulas for energy,

static power, dynamic power, integrated circuit costs, reliability, and availability.

(These formulas are also found on the front inside cover.) Our hope is that these

topics can be used through the rest of the book. In addition to the classic quantita-

tive principles of computer design and performance measurement, the PIAT sec-

tion has been upgraded to use the new SPECPower benchmark.

Our view is that the instruction set architecture is playing less of a role today

than in 1990, so we moved this material to Appendix A. It still uses the MIPS64

architecture. (For quick review, a summary of the MIPS ISA can be found on the

back inside cover.) For fans of ISAs, Appendix K covers 10 RISC architectures,

the 80x86, the DEC VAX, and the IBM 360/370.

We then move onto memory hierarchy in Chapter 2, since it is easy to apply

the cost-performance-energy principles to this material and memory is a critical

resource for the rest of the chapters. As in the past edition, Appendix B contains

an introductory review of cache principles, which is available in case you need it.

Chapter 2 discusses 10 advanced optimizations of caches. The chapter includes

virtual machines, which offers advantages in protection, software management,

and hardware management and play an important role in cloud computing. In

addition to covering SRAM and DRAM technologies, the chapter includes new

material on Flash memory. The PIAT examples are the ARM Cortex A8, which is

used in PMDs, and the Intel Core i7, which is used in servers.

Chapter 3 covers the exploitation of instruction-level parallelism in high-

performance processors, including superscalar execution, branch prediction,

speculation, dynamic scheduling, and multithreading. As mentioned earlier,

Appendix C is a review of pipelining in case you need it. Chapter 3 also sur-

veys the limits of ILP. Like Chapter 2, the PIAT examples are again the ARM

Cortex A8 and the Intel Core i7. While the third edition contained a great deal

xviii

■

Preface

on Itanium and VLIW, this material is now in Appendix H, indicating our view

that this architecture did not live up to the earlier claims.

The increasing importance of multimedia applications such as games and video

processing has also increased the importance of achitectures that can exploit data-

level parallelism. In particular, there is a rising interest in computing using graphi-

cal processing units (GPUs), yet few architects understand how GPUs really work.

We decided to write a new chapter in large part to unveil this new style of com-

puter architecture. Chapter 4 starts with an introduction to vector architectures,

which acts as a foundation on which to build explanations of multimedia SIMD

instrution set extensions and GPUs. (Appendix G goes into even more depth on

vector architectures.) The section on GPUs was the most difficult to write in this

book, in that it took many iterations to get an accurate description that was also

easy to understand. A significant challenge was the terminology. We decided to go

with our own terms and then provide a translation between our terms and the offi-

cial NVIDIA terms. (A copy of that table can be found in the back inside cover

pages.) This chapter introduces the Roofline performance model and then uses it

to compare the Intel Core i7 and the NVIDIA GTX 280 and GTX 480 GPUs. The

chapter also describes the Tegra 2 GPU for PMDs.

Chapter 5 describes multicore processors. It explores symmetric and

distributed-memory architectures, examining both organizational principles and

performance. Topics in synchronization and memory consistency models are

next. The example is the Intel Core i7. Readers interested in interconnection net-

works on a chip should read Appendix F, and those interested in larger scale mul-

tiprocessors and scientific applications should read Appendix I.

As mentioned earlier, Chapter 6 describes the newest topic in computer archi-

tecture, warehouse-scale computers (WSCs). Based on help from engineers at

Amazon Web Services and Google, this chapter integrates details on design, cost,

and performance of WSCs that few architects are aware of. It starts with the pop-

ular MapReduce programming model before describing the architecture and

physical implemention of WSCs, including cost. The costs allow us to explain

the emergence of cloud computing, whereby it can be cheaper to compute using

WSCs in the cloud than in your local datacenter. The PIAT example is a descrip-

tion of a Google WSC that includes information published for the first time in

this book.

This brings us to Appendices A through L. Appendix A covers principles of

ISAs, including MIPS64, and Appendix K describes 64-bit versions of Alpha,

MIPS, PowerPC, and SPARC and their multimedia extensions. It also includes

some classic architectures (80x86, VAX, and IBM 360/370) and popular embedded

instruction sets (ARM, Thumb, SuperH, MIPS16, and Mitsubishi M32R). Appen-

dix H is related, in that it covers architectures and compilers for VLIW ISAs.

As mentioned earlier, Appendices B and C are tutorials on basic caching and

pipelining concepts. Readers relatively new to caching should read Appendix B

before Chapter 2 and those new to pipelining should read Appendix C before

Chapter 3.

Preface

■

xix

Appendix D, “Storage Systems,” has an expanded discussion of reliability and

availability, a tutorial on RAID with a description of RAID 6 schemes, and rarely

found failure statistics of real systems. It continues to provide an introduction to

queuing theory and I/O performance benchmarks. We evaluate the cost, perfor-

mance, and reliability of a real cluster: the Internet Archive. The “Putting It All

Together” example is the NetApp FAS6000 filer.

Appendix E, by Thomas M. Conte, consolidates the embedded material in one

place.

Appendix F, on interconnection networks, has been revised by Timothy M.

Pinkston and José Duato. Appendix G, written originally by Krste Asanovic´, includes

a description of vector processors. We think these two appendices are some of the

best material we know of on each topic.

Appendix H describes VLIW and EPIC, the architecture of Itanium.

Appendix I describes parallel processing applications and coherence protocols

for larger-scale, shared-memory multiprocessing. Appendix J, by David Gold-

berg, describes computer arithmetic.

Appendix L collects the “Historical Perspective and References” from each

chapter into a single appendix. It attempts to give proper credit for the ideas in

each chapter and a sense of the history surrounding the inventions. We like to

think of this as presenting the human drama of computer design. It also supplies

references that the student of architecture may want to pursue. If you have time,

we recommend reading some of the classic papers in the field that are mentioned

in these sections. It is both enjoyable and educational to hear the ideas directly

from the creators. “Historical Perspective” was one of the most popular sections

of prior editions.

Navigating the Text

There is no single best order in which to approach these chapters and appendices,

except that all readers should start with Chapter 1. If you don’t want to read

everything, here are some suggested sequences:

■

Memory Hierarchy: Appendix B, Chapter 2, and Appendix D.

■

Instruction-Level Parallelism: Appendix C, Chapter 3, and Appendix H

■

Data-Level Parallelism: Chapters 4 and 6, Appendix G

■

Thread-Level Parallelism: Chapter 5, Appendices F and I

■

Request-Level Parallelism: Chapter 6

■

ISA: Appendices A and K

Appendix E can be read at any time, but it might work best if read after the ISA

and cache sequences. Appendix J can be read whenever arithmetic moves you.

You should read the corresponding portion of Appendix L after you complete

each chapter.

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