《算法：顺序与并行 - 统一方法》第二版简介

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"《算法：顺序与并行 - 统一方法，第二版》是一本专注于算法设计与分析的专业教材。本书提供了一种全面且统一的方法来理解和处理序列（sequential）和并行（parallel）算法。作者通过该版本强调，尽管内容是英文而非影印版，但其核心目标是确保读者能够掌握算法的基本原理和技术。在书中，作者对算法进行了深入探讨，包括但不限于排序、搜索、图论等经典问题的解决策略。读者将学习如何分析算法的时间复杂度和空间复杂度，这对于理解算法效率至关重要。对于并行算法部分，书籍介绍了如何利用多核处理器、分布式系统和GPU等现代硬件平台实现并行计算，以及如何设计能在这些平台上高效运行的算法。值得注意的是，书中的所有代码和理论都基于作者和编者的最佳实践，但作者明确声明，由于技术的快速变化和编程环境的差异，读者在实际应用时可能会遇到性能问题或不尽如人意的结果。《算法：顺序与并行 - 统一方法，第二版》并不承诺所有程序的绝对性能，而是提供一个学习和探索的基础，用户需自行承担使用内容可能带来的风险，包括但不限于经济损失、利润减少或其他间接或伴随损害。此外，书中提供的保修和免责声明指出，作者、出版商以及与作品制作相关的人员不对使用书中程序、源代码或文本材料导致的任何损失负责，除非是由于材料制造缺陷或工艺问题。这表明读者在阅读和实践过程中应具备一定的独立判断力，并理解到学习和实践算法的过程可能涉及一定的探索性和不确定性。《算法：顺序与并行 - 统一方法，第二版》是一本适合计算机科学和工程专业学生，以及希望提升算法设计和并行编程能力的开发者的重要参考书籍。它不仅传授理论知识，还鼓励读者在实践中检验和优化算法，同时理解和管理潜在的风险。"

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Preface

major thrust of computer science is the design, analysis, implementation, and

scientific evaluation of algorithms to solve critical problems. In addition, new

challenges are being offered in the field of computational science and engineering,

an emerging discipline that unites computer science and mathematics with disci-

plinary expertise in biology, chemistry, physics, and other applied scientific and

engineering fields. Computational science and engineering is often referred to as

the “third science,” complementing both theoretical and laboratory science. These

multidisciplinary efforts typically require efficient algorithms that run on high-

performance (typically parallel) computers in order to generate the necessary com-

puter models and simulations.

With advances in computational science and engineering, parallel computing

continues to merge into the mainstream of computing. It is therefore critical that

students and scientists understand the application and analysis of algorithmic par-

adigms to both the (traditional) sequential model of computing and to a variety of

parallel models.

Many computer science departments offer courses in “Analysis of Algo-

rithms,” “Algorithms,” “An Introduction to Algorithms,” or “Data Structures and

Their Algorithms” at the junior or senior level. In addition, a course in “Analysis

of Algorithms” is required of most graduate students pursuing a degree in com-

puter science. Throughout the 1980s, the vast majority of these course offerings

focused on algorithms for sequential (von Neumann) computers. In fact, not until

the late-1980s did courses covering an introduction to parallel algorithms begin to

appear in research-oriented departments. Furthermore, these courses in parallel

algorithms were typically presented to advanced graduate students. However, by

the early 1990s, courses in parallel computing began to emerge at the undergradu-

ate level, especially at progressive four-year colleges.

It is interesting to note that throughout much of the 1990s, traditional algo-

rithms-based courses changed very little. Gradually, such courses began to incor-

porate a component of parallel algorithms, typically one to three weeks near the

end of the semester. During the later part of the 1990s, however, it was not uncom-

mon to find algorithms courses that contained as much as 1/3 of the material

devoted to parallel algorithms.

In this book, we take a very different approach to an algorithms-based course.

Parallel computing has moved into the mainstream, with clusters of commodity-

off-the-shelf (COTS) machines dominating the list of top supercomputers in the

world (www.top500.org), and smaller versions of such machines being exploited in

many research laboratories. Therefore, the time is right to teach a fundamental

course in algorithms that covers paradigms for both sequential and parallel models.

The approach we take in this book is to integrate the presentation of sequential

and parallel algorithms. Specifically, we employ a philosophy of presenting a par-

adigm, such as divide-and-conquer, and then discussing implementation issues for

both sequential and parallel models. Due to the fact that we present design and

analysis of paradigms for sequential and parallel models, the reader might notice

that the number of paradigms we can treat within a semester is limited when com-

pared to a traditional sequential algorithms text.

This book has been used successfully at a wide variety of colleges and

universities.

Prerequisites: We assume a basic knowledge of data structures and mathemati-

cal maturity. The reader should be comfortable with notions of a stack, queue, list,

and binary tree, at a level that is typically taught in a CS2 course. The reader should

also be familiar with fundamentals of discrete mathematics and Calculus. Specifi-

cally, the reader should be comfortable with limits, summations, and integrals.

Overview of Chapters

Background material for the course is presented in Chapters 1, 2, and 3. Chapter 1

introduces the concept of asymptotic analysis. While the reader might have seen

some of this material in a course on data structures, we present this material in a

fair amount of detail. The reader who is uncomfortable with some of the funda-

mental material from a Freshman-level Calculus sequence might want to brush up

on notions such as limits, summations and integrals, and derivatives, as they natu-

rally arise in the presentation and application of asymptotic analysis. Chapter 2

focuses on fundamentals of induction and recursion. While many students have

seen this material in previous courses in computer science and/or mathematics, we

have found it important to review this material briefly and to provide the students

with a reference for performing the necessary review. In Chapter 3, we present the

Master Method, a very useful cookbook-type of system for evaluating recurrence

equations that are common in an algorithms-based setting.

Chapter 4 presents an overview of combinational circuits and sorting net-

works. This work is used to motivate the natural use of parallel models and to

demonstrate the blending of architectural and algorithmic approaches. In Chapter

5, we introduce fundamental models of computation, including the RAM (a formal

sequential architecture) and a variety of parallel models of computation. The par-

allel models introduced include the PRAM, mesh, hypercube, and the Coarse-

Grained Multicomputer, to name a few. In addition, Chapter 5 introduces

terminology such as shared-memory and distributed-memory.

xvi Preface

The focus of Chapter 6 is the important problem of matrix multiplication,

which is considered for a variety of models of computation. In Chapter 7, we

introduce the parallel prefix operation. This is a very powerful operation with a

wide variety of applications. We discuss implementations and analysis for a num-

ber of the models presented in Chapter 5 and give sample applications. In Chapter

8, we introduce pointer jumping techniques and show how some list-based algo-

rithms can be efficiently implemented in parallel.

In Chapter 9, we introduce the powerful divide-and-conquer paradigm. We

discuss applications of divide-and-conquer to problems involving data movement,

including sorting, concurrent reads/writes, and so forth. Algorithms and their

analysis are presented for a variety of models.

Chapters 10 and 11 focus on two important application areas, namely, Compu-

tational Geometry and Image Processing. In these chapters, we focus on interest-

ing problems chosen from these important domains as a way of solidifying the

approach of this book in terms of developing machine independent solution strate-

gies, which can then be tailored for specific models, as required.

Chapter 12 focuses on fundamental graph theoretic problems. Initially, we

present standard traversal techniques, including breadth-first search, depth-first

search, and pointer jumping. We then discuss fundamental problems, including

tree contraction and transitive closure. Finally, we couple these techniques with

greedy algorithms to solve problems, such as labeling the connected components

of a graph, determining a minimal spanning forest of a graph, and problems

involving shortest or minimal-weight paths in a graph.

Chapter 13 is an optional chapter concerned with some fundamental numeri-

cal problems. The focus of the chapter is on sequential algorithms for polynomial

evaluation and approximations of definite integrals.

Recommended Use

This book has been successfully deployed in both elective and required courses,

with students typically ranging from juniors (3rd-year undergraduates) to 2nd-year

graduates. A student in a course using this book need not be advanced in a mathe-

matical sense, but should have a basic, fundamental, background.

Correspondence

Please feel free to contact the authors directly with any comments or criticisms

(constructive or otherwise) of this book. Russ Miller may be reached at

miller@buffalo.edu and Laurence Boxer may be reached at boxer@niagara.edu.

In addition, a Web site for the book can be found from http://www.cse.buffalo.

edu/pub/WWW/faculty/miller/research.htm. This Web site contains information

related to the book, including pointers to education-based pages, relevant parallel

computing links, and errata.

Preface xvii

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