A New Vision and Role
Content Overview
Classroom Use
Online Resources

Concurrent programming originated in 1962 with the invention of channels, which are independent device controllers. Channels make it possible to have a CPU execute a new application program at the same time that I/O operations are being executed on behalf of other, suspended application programs. Hence, concurrent programming---the word ``concurrent'' means happening at the same time---was initially of concern to operating systems designers. In the late 1960s hardware designers developed multiple processor machines. This presented not only a challenge for operating systems designers but also an opportunity that application programmers could exploit.

The first major concurrent programming challenge was to solve what is now called the critical section problem. It and related problems (dining philosophers, readers/writers, etc.) led to a spate of papers in the 1960s. To harness the challenge, people developed synchronization primitives such as semaphores and monitors to simplify the programmer's task. Shortly later, in the mid 1970s, people came to appreciate the necessity of using formal methods to help control the inherent complexity of concurrent programs.

Computer networks were introduced starting in the late 1970s. The Arpanet made possible wide-area computing, and the Ethernet made possible local-area networks. Networks gave rise to distributed programming, which was a major topic of the 1980s and became even more important in the 1990s. The essence of distributed programming is that processes interact by means of message passing rather than by reading and writing shared variables.

Now, at the dawn of a new century, we have seen the emergence of massively parallel processing---in which tens, hundreds, or even thousands of processors are used to solve a single problem. We have also seen the emergence of client/server computing, the Internet, and the World Wide Web. Finally, we are beginning to see multiprocessor workstations and PCs. Concurrent hardware is more prevalent than ever, and concurrent programming is more relevant than ever.

This is my third book, another attempt to capture a part of the history of concurrent programming. My first book---Concurrent Programming: Principles and Practice, published in 1991---gives a broad, reference-level coverage of the period between 1960 and 1990. Because new problems, programming mechanisms, and formal methods were large parts of those decades, the book emphasizes those topics.

My second book---IThe SR Programming Language: Concurrency in Practice, published in 1993---summarizes my work with Ron Olsson in the late 1980s and early 1990s on a specific language that can be used to write concurrent programs for both shared- and distributed-memory machines. The SR book is pragmatic rather than formal, showing how to solve lots of problems in a single programming language.

This book is an outgrowth of my past and a reflection of what I think is important now and into the future. I have drawn heavily from material in the Concurrent Programming book, but I have completely rewritten every section that I have retained, and have rewritten examples to use pseudo-C rather than SR. I have added new material throughout, especially on parallel scientific programming. I have also included case studies of the most important languages and software libraries, with complete sample programs. Finally, I have a new vision for the role of this book---in classrooms and in personal libraries.

A New Vision and Role

Parallel and distributed computing are today pervasive concepts. As usual in computer science, advances have been led by hardware designers, who keep building bigger, faster, more powerful computers and communication networks. For the most part, they are succeeding---witness the stock market!

New computers and networks create new challenges and opportunities, and for once software designers are no longer all that far behind. Consider Web browsers, Internet commerce, Pthreads, Java, MPI, and so on---and again, witness the stock market! These software products are specifically designed to take advantage of concurrency in hardware and applications. In short, much of the computing world is now concurrent!

Aspects of concurrent computing---whether multithreaded, parallel, or distributed---are now covered in almost every advanced course in Computer Science. Reflecting the history of the topic, operating systems courses lead the way---covering topics like multithreading, communication protocols, and distributed file systems. Architecture courses cover multiprocessors and networks. Compilers courses cover compilation issues for parallel machines. Theory courses cover models for parallel computing. Algorithms courses cover parallel algorithms. Database courses cover locking and distributed databases. Graphics courses make use of parallelism for rendering and ray tracing. The list goes on. In addition, concurrent computing has become a fundamental tool in a wide range of science and engineering disciplines.

The main purpose of this book---as reflected in the title---is to lay the foundations for programming multithreaded, parallel, and distributed computations. The specific goal is to provide a text that can be used for, or to create, an advanced undergraduate/beginning graduate course. Whenever a topic in computing has become pervasive, as concurrency surely has, we have added foundation courses to provide students with basic knowledge of a topic. Similarly, whenever a topic has become well understood, as concurrency now is, we have migrated the topic to the core curriculum.

I have tried to cover those aspects of parallel and distributed computing that I think every computer science student should know. This includes basic principles, programming techniques, major applications, implementations, and performance issues. In addition, I have included case studies of many of the most important languages and software libraries: Pthreads (three chapters), Java (three chapters), CSP, Linda, MPI (two chapters), Ada, SR, and OpenMP. Each case study describes relevant parts of the language or library and then presents a complete sample program. (Source for the programs is available on the book's Web site, as described below.) In addition, I summarize several additional languages, models, and tools for parallel scientific computing in Chapter 12.

On the other hand, no one book can cover everything---and still be affordable---so students and instructors may wish to augment this text with others. The Historical Notes and the References at the end of each chapter describe additional material and provide pointers for further study. The book's Web pages (see below) provide further information and links to relevant material.

Content Overview

This book contains 12 chapters. Chapter 1 summarizes basic concepts of concurrency, hardware, and applications. Then it examines five representative applications: matrix multiplication, adaptive quadrature, Unix pipes, remote files, and distributed matrix multiplication. Each application is simple yet useful; together they illustrate the range of issues and five recurring programming styles. The last section of Chapter 1 summarizes the programming notation that is used in the text.

The remaining chapters are organized into three parts. Part 1 describes concurrent programming mechanisms that use shared variables, and hence that are directly suitable for shared-memory machines. Chapter 2 introduces fundamental concepts of processes and synchronization; the chapter uses a series of examples to illustrate the key points and ends with a discussion of the formal semantics of concurrency. Understanding the semantic concepts will help you understand parts of later chapters, but it should also be sufficient to refer back to them when necessary. (The index provides pointers to all key concepts, and the glossary gives definitions of all key terms.) Chapter 3 shows how to implement and use locks and barriers; it also describes data parallel algorithms and a parallel programming technique called a bag of tasks. Chapter 4 describes semaphores and gives numerous examples of how to use them. Semaphores were the first high-level concurrent programming mechanism and remain one of the most important. Chapter 5 covers monitors in detail. Monitors were introduced in a seminal 1974 paper, somewhat lost favor in the 1980s and early 90s, but have regained importance with the Java language. Finally, Chapter 6 describes how to implement processes, semaphores, and monitors on both uniprocessors and multiprocessors.

Part 2 covers distributed programming, in which processes communicate and synchronize by means of messages. Chapter 7 describes message passing using send and receive primitives. It shows how to use these primitives to program filters (which have one-way communication), clients and servers (which have two-way communication), and interacting peers (which have back and forth communication). Chapter 8 examines two additional communication primitives: remote procedure call (RPC) and rendezvous. With these, a client process initiates a communication by issuing a call---which is implicitly a send followed by a receive; the communication is serviced either by a new process (RPC) or by a rendezvous with an existing process. Chapter 9 describes several paradigms for process interaction in distributed programs. These include three that are commonly used in parallel computations---manager/workers, heartbeat, and pipeline---as well as four that arise in distributed systems---probe/echo, broadcast, token passing, and replicated servers. Finally, Chapter 10 describes how to implement message passing, RPC, and rendezvous. That chapter also shows how to implement what is called a distributed shared memory, which supports a shared-memory programming model in a distributed environment.

Part 3 covers parallel programming, especially for high-performance scientific computations. (Many other kinds of parallel computations are described in earlier chapters and in the exercises of several chapters.) The goal of a parallel program is speedup, namely to solve a problem faster by using multiple processors. Parallel programs are written using shared variables or message passing; hence they employ the techniques described in Parts I and II. Chapter 11 examines the three major classes of scientific computing applications: grid, particle, and matrix computations. These arise in simulating (modeling) physical and biological systems; matrix computations are also used for such things as economic forecasting. Chapter 12 surveys the most important tools that are used to write parallel scientific computations: libraries (Pthreads, MPI, and OpenMP), parallelizing compilers, languages and models, and higher-level tools such as metacomputations.

The end of each chapter provides historical notes, references, and an extensive set of exercises. The historical notes summarize the origin and evolution of each topic and how the topics relate to each other. The notes also describe the papers and books listed in the reference section. The exercises explore the topics covered in each chapter as well as introduce additional applications.

Classroom Use

I use this book each spring at The University of Arizona for a class of about 60 students. About half the students are undergraduates and half are graduates. Most are Computer Science majors; the class is an elective for them, but most majors end up taking it. The class is also taken each year by a few Ph.D. students from other departments who have an interest in computational science and parallel computing. Most of the undergraduates are simultaneously taking our undergraduate operating systems course; the CS graduate students have all had such a course and many have also taken our advanced operating systems course.

Our operating systems classes, like those elsewhere, examine the synchronization needs of operating systems, and explore how processes, synchronization, and other OS topics are implemented, mostly on single processor machines. My class shows how to use concurrency as a general programming tool in a broad range of applications. I look at programming techniques, higher level concepts, parallel computing, and distributed computing. In a sense my class is to an OS class somewhat like a comparative programming languages class is to a compilers class.

In the first half of my class, I cover Chapter 2 in some detail, but then go fairly rapidly through the other chapters in Part 1, emphasizing topics not covered in the OS classes: Test and Test and Set, the Ticket Algorithm, barriers, passing the baton for semaphores, some of the monitor programming techniques, and the multiprocessor kernel. In addition, students are introduced to the Pthreads library and Java's threads and synchronized methods in homework assignments. I also include a parallel computing project after my lectures on barriers (drawing material from Chapter 11).

In the second half of my class I cover much of Part 2 of the book on distributed programming. We look at message passing, and how it is used to program both distributed systems and distributed parallel computations. Then we look at RPC and rendezvous, their realization in Java and Ada, and distributed systems applications. Finally, we look at each of the paradigms in Chapter 9, again using applications from parallel and distributed systems to motivate and illustrate them. Students get introduced to the MPI library in a homework assignment and again use Java.

During the term I give four homework assignments, two in-class exams, a parallel computing project, and a final project. (See the book's Web site for samples.) Each homework assignment consists of written problems from the exercises, as well as one or two programming problems. Graduate students are required to do all problems; undergraduates do two-thirds of the problems (of their choice). Exams are administered similarly: graduates do all problems, undergraduates choose a subset of the problems to answer. At Arizona we use SR in the earliest programming assignments, and students have the option of using SR for many of the later assignments, but they are encouraged to use Pthreads, Java, and MPI.

The parallel programming project addresses one of the problems in Chapter 11 (often a grid computation). Students write programs and do timing experiments on a small shared-memory multiprocessor. Graduate students implement harder algorithms and also are required to write a fairly detailed report on their experiments. The final project is a paper or programming project on some aspect of distributed computing. Students get to choose what they want to do, subject to my approval. Most students do implementation projects; many work in pairs. The students produce an amazing variety of interesting systems, and most include a graphical user interface.

Even though the students in my class have a wide range of backgrounds, almost all give the class an excellent evaluation. Undergraduates frequently remark on how well it meshes with their OS class; they like seeing concurrency from another perspective, learning about multiprocessors, and seeing a broad range of applications and tools. Graduate students remark on how the class ties together lots of things they had heard something about, and also introduces them to the breadth of concurrent programming; many of them would, however, like to see more coverage of parallel computing. Eventually we hope to be able to offer separate classes for undergraduates and graduates. I would not change much for the undergraduate-only class, but I would cover slightly fewer topics in somewhat more depth. For the graduate version I would spend less time on stuff they had seen (Part 1) and more time on parallel applications and parallel computing tools (Part 3).

This book is ideally suited for classes that cover the range of concurrent programming mechanisms, tools, and applications. Almost all students will make use of some of what is covered here, but not many will do just parallel computing, just distributed systems, or just programming in Java. However, the book can also be used as a supplement to classes with a more specialized focus. For example, a parallel computing class might use this book together with one that covers parallel algorithms and applications.

Online Resources

The ``home page'' for the book is located at:
The Web site contains the source for the larger programs in the text, links to software and information on the languages and libraries described in the case studies, and a variety of other useful links. It also contains lots of information from my Arizona class, including the syllabus, lecture notes, and copies of homework assignments, projects, and exams. Despite my best efforts, this book no doubt contains errors, so there will be (too soon I'm sure) an errata listing. Other material will no doubt be added in the future.


I received many useful suggestions from the reviewers of earlier drafts of this book. Martti Tienari and his students at the University of Helsinki found several subtle errors. My most recent Ph.D. students, Vince Freeh and Dave Lowenthal, provided comments on new chapters and helped debug my thinking, if not my programs, over the past several years. The students in my spring 1999 class served as guinea pigs and found several embarrassing errors. Andy Bernat also provided feedback from his spring 1999 class at the University of Texas, El Paso.

The National Science Foundation has supported my research for many years, most recently through grants CCR-9415303 and ACR-9720738. A research infrastructure grant from NSF, CDA-9500991, helped support the equipment used to prepare the book and test the programs.

Most importantly, I want to thank my wife, Mary, who once again put up with the thousands of hours I spent working on this book---despite having vowed ``never again'' after finishing the SR book.

Greg Andrews
Tucson, Arizona