The Art of Multiprocessor Programming, Revised Reprint

Maurice Herlihy, Nir Shavit

• Editorial: Morgan Kaufmann
• Año: 2012
• ISBN:
  9780123977953
• Categoría: Computer Science
• Categoría: Systems Architecture

Revised and updated with improvements conceived in parallel programming courses, The Art of Multiprocessor Programming is an authoritative guide to multicore programming. It introduces a higher level set of software development skills than that needed for efficient single-core programming. This book provides comprehensive coverage of the new principles, algorithms, and tools necessary for effective multiprocessor programming. Students and professionals alike will benefit from thorough coverage of key multiprocessor programming issues.

• This revised edition incorporates much-demanded updates throughout the book, based on feedback and corrections reported from classrooms since 2008
• Learn the fundamentals of programming multiple threads accessing shared memory
• Explore mainstream concurrent data structures and the key elements of their design, as well as synchronization techniques from simple locks to transactional memory systems
• Visit the companion site and download source code, example Java programs, and materials to support and enhance the learning experience

1

Introduction

The computer industry is undergoing, if not another revolution, certainly a vigorous shaking-up. The major chip manufacturers have, for the time being at least, given up trying to make processors run faster. Moore’s Law has not been repealed: each year, more and more transistors fit into the same space, but their clock speed cannot be increased without overheating. Instead, manufacturers are turning to “multicore” architectures, in which multiple processors (cores) communicate directly through shared hardware caches. Multiprocessor chips make computing more effective by exploiting parallelism: harnessing multiple processors to work on a single task.

The spread of multiprocessor architectures will have a pervasive effect on how we develop software. Until recently, advances in technology meant advances in clock speed, so software would effectively “speed up” by itself over time. Now, however, this free ride is over. Advances in technology will mean increased parallelism and not increased clock speed, and exploiting such parallelism is one of the outstanding challenges of modern Computer Science.

This book focuses on how to program multiprocessors that communicate via a shared memory. Such systems are often called shared-memory multiprocessors or, more recently, multicores. Programming challenges arise at all scales of multiprocessor systems—at a very small scale, processors within a single chip need to coordinate access to a shared memory location, and on a large scale, processors in a supercomputer need to coordinate the routing of data. Multiprocessor programming is challenging because modern computer systems are inherently asynchronous: activities can be halted or delayed without warning by interrupts, preemption, cache misses, failures, and other events. These delays are inherently unpredictable, and can vary enormously in scale: a cache miss might delay a processor for fewer than ten instructions, a page fault for a few million instructions, and operating system preemption for hundreds of millions of instructions.

We approach multiprocessor programming from two complementary directions: principles and practice. In the principles part of this book, we focus on computability: figuring out what can be computed in an asynchronous concurrent environment. We use an idealized model of computation in which multiple concurrent threads manipulate a set of shared objects. The sequence of the thread operations on the objects is called the concurrent program or concurrent algorithm. This model is essentially the model presented by the Java™, C#, or C++ thread packages.

Surprisingly, there are easy-to-specify shared objects that cannot be implemented by any concurrent algorithm. It is therefore important to understand what not to try, before proceeding to write multiprocessor programs. Many of the issues that will land multiprocessor programmers in trouble are consequences of fundamental limitations of the computational model, so we view the acquisition of a basic understanding of concurrent shared-memory computability as a necessary step. The chapters dealing with principles take the reader through a quick tour of asynchronous computability, attempting to expose various computability issues, and how they are addressed through the use of hardware and software mechanisms.

An important step in the understanding of computability is the specification and verification of what a given program actually does. This is perhaps best described as program correctness. The correctness of multiprocessor programs, by their very nature, is more complex than that of their sequential counterparts, and requires a different set of tools, even for the purpose of “informal reasoning” (which, of course, is what most programmers actually do). Sequential correctness is mostly concerned with safety properties. A safety property states that some “bad thing” never happens. For example, a traffic light never displays green in all directions, even if the power fails. Naturally, concurrent correctness is also concerned with safety, but the problem is much, much harder, because safety must be ensured despite the vast number of ways that the steps of concurrent threads can be interleaved. Equally important, concurrent correctness encompasses a variety of liveness properties that have no counterparts in the sequential world. A liveness property states that a particular good thing will happen. For example, a red traffic light will eventually turn green. A final goal of the part of the book dealing with principles is to introduce a variety of metrologies and approaches for reasoning about concurrent programs, which will later serve us when discussing the correctness of real-world objects and programs.

The second part of the book deals with the practice of multiprocessor programming, and focuses on performance. Analyzing the performance of multiprocessor algorithms is also different in flavor from analyzing the performance of sequential programs. Sequential programming is based on a collection of well-established and well-understood abstractions. When we write a sequential program, we usually do not need to be aware that underneath it all, pages are being swapped from disk to memory, and smaller units of memory are being moved in and out of a hierarchy of processor caches. This complex memory hierarchy is essentially invisible, hiding behind a simple programming abstraction.

In the multiprocessor context, this abstraction breaks down, at least from a performance perspective. To achieve adequate performance, the programmer must sometimes “outwit” the underlying memory system, writing programs that would seem bizarre to someone unfamiliar with multiprocessor architectures. Someday perhaps, concurrent architectures will provide the same degree of efficient abstraction now provided by sequential architectures, but in the meantime, programmers should beware.

The principles part of the book presents a progressive collection of shared objects and programming tools. Every object and tool is interesting in its own right, and we use each one to expose the reader to higher-level issues: spin-locks illustrate contention, linked lists illustrate the role of locking in data structure design, and so on. Each of these issues has important consequences for program performance. The hope is that the reader will understand the issue in a way that will later allow him or her to apply the lessons learned to specific multiprocessor systems. We culminate with a discussion of state-of-the-art technologies such as transactional memory.

We would like to include a few words about style. The book uses the Java programming language. There are, of course, other suitable languages which readers would have found equally appealing. We have a long list of reasons for our specific choice, but perhaps it is more suitable to discuss them over a cup of coffee! In the appendix we explain how the concepts expressed in Java are expressed in other popular languages or libraries. We also provide a primer on multiprocessor hardware. Throughout the book, we avoid presenting specific performance numbers for programs and algorithms, and stick to general trends. There is a good reason for this: multiprocessors vary greatly, and unfortunate though it may be, at this point in time, what works well on one machine may be significantly less impressive on another. Sticking to general trends is our way of guaranteeing that the validity of our assertions will be sustained over time.

We provide references at the end of each chapter. The reader will find a bibliographical survey of the material covered, with suggestions for further reading. Each chapter also includes a collection of exercises which readers can use to gauge their comprehension or entertain themselves on Sunday mornings.

1.1 Shared Objects and Synchronization

On the first day of your new job, your boss asks you to find all primes between 1 and 10¹⁰ (never mind why), using a parallel machine that supports ten concurrent threads. This machine is rented by the minute, so the longer your program takes, the more it costs. You want to make a good impression. What do you do?

As a first attempt, you might consider giving each thread an equal share of the input domain. Each thread might check 10⁹ numbers, as shown in Fig. 1.1. This approach fails, for an elementary, but important reason. Equal ranges of inputs do not necessarily produce equal amounts of work. Primes do not occur uniformly: there are more primes between 1 and 10⁹ than between 9.10⁹ and 10¹⁰. To make matters worse, the computation time per prime is not the same in all ranges: it usually takes longer to test whether a large number is prime than a small number. In short, there is no reason ...