使用OpenMP进行并行编程：科学与工程计算实践

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"《Using OpenMP: Portable Shared Memory Parallel Programming》是一本专注于OpenMP并行编程的书籍，书中包含丰富的实战经验，适用于科学与工程计算领域。该书由William Gropp和Ewing Lusk编辑，旨在帮助读者理解和掌握OpenMP在多线程编程中的应用，以提高计算效率。书中可能涵盖了OpenMP的基础概念、API用法、并行算法设计以及性能优化策略等内容。此外，提及的其他书籍展示了不同年代的并行计算技术，如Data-Parallel Programming、PVM（Parallel Virtual Machine）以及High-Performance Fortran等，这些都反映了并行计算技术的发展历程和技术多样性。" OpenMP是用于共享内存并行计算的一种流行标准，它允许程序员通过添加编译器指令来实现并行化，尤其适合于C++、C和Fortran等编程语言。本书《Using OpenMP》深入探讨了如何有效地利用OpenMP进行多线程编程，帮助开发者构建可移植、高效的并行程序。并行编程是解决计算密集型问题的关键，特别是在科学和工程计算领域，它能够显著提升计算速度，缩短任务完成时间。OpenMP以其简洁的接口和良好的跨平台性，成为了许多科研人员和工程师首选的并行编程工具。通过学习本书，读者可以掌握以下关键知识点： 1. **OpenMP基础**：理解OpenMP的基本结构，包括并行区域（`parallel`）、任务（`task`）、同步（`barrier`、`mutex`等）和循环并行化（`parallel for`）。 2. **线程管理和调度**：了解如何控制并行线程的数量，以及如何使用动态或静态调度策略。 3. **数据并行**：学习如何处理并行环境下的数据共享和私有化，以及如何使用`collapse`和`schedule`子句优化数据并行操作。 4. **并行算法设计**：掌握如何将串行算法转换为并行算法，特别是在数值计算和科学计算中的应用。 5. **性能分析和优化**：学习使用性能分析工具，识别和解决并行程序中的瓶颈，以及如何通过调整工作量分配和内存访问模式来优化性能。 6. **错误处理和调试**：学习在并行环境中如何处理异常和错误，以及如何使用OpenMP的调试工具进行问题定位。 7. **并行环境的考虑**：理解多处理器系统和分布式内存系统的特性，以及如何在这些环境中有效地部署OpenMP程序。 8. **实战案例**：书中提供的实战经验可以帮助读者将理论知识应用于实际项目，解决实际问题。通过阅读这本书，读者不仅可以获得OpenMP的理论知识，还能学习到作者在实际并行计算项目中积累的经验，这对于提升并行编程能力非常有益。对于那些在科研、工程领域工作，并希望利用多核处理器提升计算效率的程序员来说，《Using OpenMP》是一本不可多得的参考书。

Foreword

Programming languages evolve just as natural languages do, driven by human de-

sires to express thoughts more cleverly, succinctly, or elegantly than in the past.

A big diﬀerence is the fact that one key receiver of programs is nonhuman. These

nonhumans evolve faster than humans do, helping drive language mutation after

mutation, and—together with the human program writers and readers—naturally

selecting among the mutations.

In the 1970s, vector and parallel computer evolution was on the move. Program-

ming assistance was provided by language extensions—ﬁrst to Fortran and then

to C—in the form of directives and pragmas, respectively. Vendors diﬀerentiated

themselves by providing “better” extensions than did their competitors; and by

the mid-1980s things had gotten out of hand for software vendors. At Kuck and

Associates (KAI), we had the problem of dealing with the whole industry, so Bruce

Leasure and I set out to ﬁx things by forming an industrywide committee, the

Parallel Computing Forum (PCF). PCF struck a nerve and became very active.

In a few years we had a draft standard that we took through ANSI, and after a

few more years it became the ANSI X3.H5 draft. Our stamina gave out before it

became an oﬃcial ANSI standard, but the industry paid attention, and extensions

evolved more uniformly.

This situation lasted for a few years, but the 1980s were a golden era for parallel

architectural evolution, with many people writing parallel programs, so extensions

again diverged, and programming needs grew. KAI took on the challenge of re-

thinking things and deﬁning parallel proﬁling and correctness-checking tools at the

same time, with the goal of innovative software development products. By the

mid-1990s we had made a lot of progress and had discussed it a bit with some

hardware vendors. When SGI bought Cray in April 1996, they had an immediate

directive problem (two distinct extensions) and approached us about working with

them. Together we reﬁned what we had, opened up to the industry, and formed

the Architecture Review Board (ARB). OpenMP was born 18 months later, as the

New York Times reported:

NEW STANDARD FOR PARALLEL PROCESSING WORKSTATIONS

Compaq, Digital, Intel, IBM and Silicon Graphics have agreed to

support OpenMP, a new standard developed by Silicon Graphics and

Kuck & Associates to allow programmers to write a single version

of their software that will run on parallel processor computers

using Unix or Windows NT operating systems. The new standard will

xvi Foreword

hasten the trend in which scientists and engineers choose high-end

workstations rather than supercomputers for complex computational

applications. (NYT 28 Oct. 1997)

OpenMP has been adopted by many software developers in the past decade, but it

has competed with traditional hand threading at the one extreme and MPI at the

other. These alternatives are much lower-level expressions of parallelism: threading

allows more control, MPI more scalability. Both usually require much more initial

eﬀort to think through the details of program control, data decomposition, and

expressing thoughts with assembly-language-style calls. The multicore revolution

now demands simple parallel application development, which OpenMP provides

with language extensions and tools. While OpenMP has limitations rooted in its

technical origins, the ARB continues to drive the standard forward.

The supercomputing needs of the New York Times article have now been largely

replaced by scalable clusters of commodity multicore processors. What was a work-

station is now a desktop or laptop multicore system. The need for eﬀective parallel

software development continues to grow in importance.

This book provides an excellent introduction to parallel programming and Open-

MP. It covers the language, the performance of OpenMP programs (with one hun-

dred pages of details about Fortran and C), common sources of errors, scalability

via nested parallelism and combined OpenMP/MPI programs, OpenMP implemen-

tation issues, and future ideas. Few books cover the topics in this much detail; it

includes the new OpenMP 2.5 speciﬁcation, as well as hints about OpenMP 3.0

discussions and issues.

The book should be welcomed by academia, where there is rising interest in un-

dergraduate parallel programming courses. Today, parallel programming is taught

in most universities, but only as a graduate course. With multicore processors now

used everywhere, introductory courses need to add parallel programming. Because

performance is little discussed in any undergraduate programming courses today,

parallel programming for performance is hard to incorporate. OpenMP helps to

bridge this gap because it can be added simply to sequential programs and comes

with multiple scheduling algorithms that can easily provide an experimental ap-

proach to parallel performance tuning.

OpenMP has some deceptive simplicities, both good and bad. It is easy to start

using, placing substantial burden on the system implementers. In that sense, it puts

oﬀ some experienced users and beginners with preconceived ideas about POSIX or

WinThreads, who decide that parallel programming can’t be that simple and who

want to indicate on which processor each thread is going to run (and other unnec-

Foreword xvii

essary details). OpenMP also allows for very strong correctness checking versus

the correctness of the sequential program to which OpenMP directives are added.

Intel Thread Checker and other tools can dynamically pinpoint, to the line num-

ber, most OpenMP parallel programming bugs. Thus, OpenMP implementations

indeed remove annoying burdens from developers. This book will help educate the

community about such beneﬁts.

On the other hand, the simplicity of getting started with OpenMP can lead

one to believing that any sequential program can be made into a high-performance

parallel program, which is not true. Architectural and program constraints must be

considered in scaling up any parallel program. MPI forces one to think about this

immediately and in that sense is less seductive than OpenMP. However, OpenMP

scalability is being extended with nested parallelism and by Intel’s ClusterOpenMP

with new directives to distinguish shared- and distributed-memory variables. In

the end, a high-performance OpenMP or OpenMP/MPI program may need a lot

of work, but getting started with OpenMP remains quite easy, and this book treats

the intricacies of scaling via nesting and hybrid OpenMP/MPI.

OpenMP is supported by thriving organizations. The ARB membership now in-

cludes most of the world’s leading computer manufacturers and software providers.

The ARB is a technical body that works to deﬁne new features and ﬁx problems.

Any interested programmer can join cOMPunity, a forum of academic and industrial

researchers and developers who help drive the standard forward.

I am pleased that the authors asked me to write this foreword, and I hope that

readers learn to use the full expressibility and power of OpenMP. This book should

provide an excellent introduction to beginners, and the performance section should

help those with some experience who want to push OpenMP to its limits.

David J. Kuck

Intel Fellow, Software and Solutions Group

Director, Parallel and Distributed Solutions

Intel Corporation

Urbana, IL, USA

March 14, 2007

Preface

At Supercomputing 1997, a major conference on High Performance Computing,

Networking, and Storage held in San Jose, California, a group of High Performance

Computing experts from industry and research laboratories used an informal “Birds

of a Feather” session to unveil a new, portable programming interface for shared-

memory parallel computers. They called it OpenMP. The proposers included repre-

sentatives from several hardware companies and from the software house Kuck and

Associates, as well as scientists from the Department of Energy who wanted a way

to write programs that could exploit the parallelism in shared memory machines

provided by several major hardware manufacturers.

This initiative could not have been more timely. A diversity of programming

models for those early shared-memory systems were in use. They were all diﬀerent

enough to inhibit an easy port between them. It was good to end this undesirable

situation and propose a uniﬁed model.

A company was set up to own and maintain the new informal standard. It

was named the OpenMP Architecture Review Board (ARB). Since that time, the

number of vendors involved in the speciﬁcation and maintenance of OpenMP has

steadily grown. There has been increased involvement of application developers,

compiler experts, and language specialists in the ARB too.

The original proposal provided directives, a user-level library, and several environ-

ment variables that could be used to turn Fortran 77 programs into shared-memory

parallel programs with minimal eﬀort. Fairly soon after the ﬁrst release, the speci-

ﬁcation was further developed to enable its use with C/C++ programs and to take

features of Fortran 90 more fully into account. Since then, the bindings for Fortran

and C/C++ have been merged, both for simplicity and to ensure that they are as

similar as possible. Over time, support for OpenMP has been added to more and

more compilers. So we can safely say that today OpenMP provides a compact,

yet ﬂexible shared-memory programming model for Fortran, C, and C++ that is

widely available to application developers.

Many people collaborated in order to produce the ﬁrst speciﬁcation of OpenMP.

Since that time, many more have worked hard in the committees set up by the

ARB to clarify certain features of the language, to consider extensions, and to

make their implementations more compatible with each other. Proposals for a

standard means to support interactions between implementations and external tools

have been intensively debated. Ideas for new features have been implemented in

research prototypes. Other people have put considerable eﬀort into promoting the

use of OpenMP and in teaching novices and experts alike how to utilize its features

to solve a variety of programming needs. One of the authors founded a not-for-

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冠诚-IBM中国研究院

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