PDF Ebook Foundations of Multithreaded, Parallel, and Distributed Programming
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Foundations of Multithreaded, Parallel, and Distributed Programming
PDF Ebook Foundations of Multithreaded, Parallel, and Distributed Programming
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Product details
Paperback: 688 pages
Publisher: Pearson; 1 edition (December 10, 1999)
Language: English
ISBN-10: 0201357526
ISBN-13: 978-0201357523
Product Dimensions:
7.5 x 1.5 x 9.1 inches
Shipping Weight: 2.6 pounds (View shipping rates and policies)
Average Customer Review:
3.4 out of 5 stars
14 customer reviews
Amazon Best Sellers Rank:
#1,031,279 in Books (See Top 100 in Books)
This book is clear, easy to read and nicely organized.The contents are summarized below:Chapter 1 begins with an introduction to concurrent computing;PART I: SHARED MEMORY Chapter 2 explains processes and synchronization, including a very easy introduction to axiomatic semantics; Chapter 3 explains locks and barriers (both use and implementation); Chapter 4 is dedicated to semaphores and their use (examples of use include mutual exclusion, barriers, producer/consumer, reader/writers); Chapter 5 is about monitors, and this is where condition variables are introduced (they're not treated separately as in POSIX, but the author does mention POSIX mutexes+cond.vars approach). Examples include bounded buffer, readers/writer, interval timer, sleeping barber, and a disck scheduling system. There is a section on Java and another one on pthreads; Chapter 6 goes into details of implementation of semaphores and monitors;PART II: DISTRIBUTED PROGRAMMING Chapter 7 is about message passing -- first asynchronous then synchronous. Case studies include CSP, Linda, MPI and Java; Chapter 8 goes into RPC and rendezvous, and case studies are Ada, SR and Java. The examples here include a remote database andsorting network; Chapter 9 deals with ways in which processes may interact. Here the author uses as examples sparse matrix multiplication, cellular automata, and other problems; Chapter 10 is about implementation details of message-passing mechanisms, RPC and distributed shared memory;PART III: PARALLEL PROGRAMMING Chapter 11 is about scientific computing (number-crunching stuff). Grid computing, particle computations, matrix computations; Chapter 12 discusses MPI, parallelzing compilers, programming languages and tools and their support for concurrent programming.Each chapter has a section with historical notes, references and LOTS of exercises.
The binding is crap, pages are falling out. Very disappointed for the $110 I spent! Can't take the time to return it because my son needs it for class!
The last textbook I purchased for college. Provides a nice overview of parallel programming. Mostly focusses on C.
Book sucks to read but you'll probably have to for your class
The content in this book is older, but the concepts are still just as relevant. I had this book for a class, and it served as an alright reference source, but I probably could have gotten through the very difficult class without it.It uses a semi-custom language for examples which takes some getting used to. The initial assumed knowledge also seemed to be just a tad high. I did not find the book to read very well as a "sit-down and read" sort of book, but as a reference for examples and paradigms it served ok.
The book was fairly damaged and had a 20 page section missing out of it. Was sufficient for the class I was taking, though.
I single out the following key points of this book:- Explanation of formal code analysis using formal logic- Explanation of the internals of IPC- An example on how to write your own OS
First a little about me and of what use I found the book. Maybe you are looking to determine whether this book is suitable for your purposes too. I studied this book cover to cover. This took a little over one year. However I have to add, to this day I did not yet solve the example problems, which are plentiful and probably carefully designed and necessary for understanding all the ins and outs and cementing the lessons into your head. But I work all day writing software for a living and felt I needed a change of pace, meaning I wanted to spend time studying and absorbing new information after work hours this year rather than writing still more code at night for now. I've done the night coding thing many a time already. I undoubtedly will shortly be writing code using the book's concepts. That's really the point of me reading it: to write a lot more concurrent code, and to do it better. To my detriment I did not have the benefit of enrolling in a real upper level undergraduate course or a graduate course as guided by an instructor as is probably more typically the case with this textbook's readers. To my benefit, during my day work I had already designed and developed some relatively successful (meaning people are actually using them today, and they paid some money for it) multithreaded, parallel, and distributed applications, such as a web statistics and analytics clustered parallel application, and communication middleware of an access control system. At mid-career it occurred to me that having already made concurrent systems by relying on my cultivated developer's intuition and OJT, now was maybe a good time to try to fill the gaps in my knowledge of concurrent programming and climb to higher levels of mastery in this specialty.Parallelism is making a strong comeback again in all sorts of systems small and large, such as GPUs and multicore CPUs as well as the giant Beowulf clusters and the classical manyprocessor supercomputers. The heat generation that comes with ever faster clock cycles has put up a pretty tough barrier in the way for the famous CPU chip makers. You can see for yourself that the rise in the gigahertz numbers has flattened out lately, and now it's the number of cores which is beginning to rise in commodity computing equipment, instead of the frequency of (serial) operations. The new parallel generation is happening now after a decade or two of diversion into emphasizing faster sequential processors, Intel and AMD being the notables in that effort. Well now the Intels and AMDs have pushed sequential processing and hardware instruction pipelining really fast but maybe they also have found the thermal limits, and the memory speed limits, and exploited all the pipelining and predictive branching that they could, and now they have to find something else to make similar progress going forward to make Mr. Moore and his Law work right again. Increasing the total aggregate throughput of operations executed across multiple processors or cores appears to be a way forward now in computing performance.I needed some good, thorough material to perform as the center pillar of a concurrent programming learning initiative.To select this book over the other textbooks that are available, whereas some seemed specialized or narrow in scope, and some were too formal or dense in their presentation for do-it-yourselfers, I noticed on the web that a pretty large number of universities are using the textbook by Andrews for their introductory course in parallel programming or concurrent programming.Now notice that the book was copyright in 2000 which is almost a decade ago now which is a bit of a limitation, yet the reality is this is not quite a problem. You can fill in the rare gap using other sources of information, like also using other books, as well as online course lecture videos graciously provided for free public consumption at web sites like the Cloudera company, and the MIT and UC Berkeley university web sites. Keep in mind there is a reason why a book becomes a classic and keeps being used at universities. Consequently however, the famous MapReduce is not really represented in the book. Globus, an earlier distributed framework, is mentioned though. Google and Yahoo and Facebook, and other such sites who are now programming innovatively in the very large for mostly nonscientific applications, would not yet hit the big time and share their concurrent computing innovations with the public until a couple more years into the future, when this book was written.In my opinion today's massively parallel applications underpinning a few of the famous web sites might well now have some of the world's biggest concurrent application clusters even rivaling supercomputers, since the "supers" don't seem to concurrently use "clusters of clusters" linked across the whole planet, like MapReduce already does every day for millions of users. The traditional supercomputers, on the other hand, even the biggest, baddest ones who are hitting the top 500 fastest lists, seem to be located at just one site at a time if I'm not mistaken. And I'm not talking about content caching or simple load balancing at Google, because GFS and Mapreduce as a parallel coordination language is much more than simple web site front ending. Mapreduce is an application development framework. I suspect Google and its globe-spanning cluster application might be even faster than any of the world's fastest single-site highly parallel supercomputers doing the atom bomb development simulations and cryptanalysis and communications traffic analytics for the DOE or military. The actual numbers are unknown it seems but I suspect the world's largest Beowulf cluster is already in use at Google and they might already be achieving application and system concurrency across perhaps a half-million compute nodes.Also, Single Instruction Multiple Data (SIMD) programming is not covered enough to my taste in Andrews book. Yet I want to program some massively parallel SIMD GPUs as seen a lot lately in daughterboards or "video cards." There is a 30-processor GPU with thousands of parallel hardware threads, organized in a multi-level thread/warp/block hierarchy, with its own separate NUMA memory subsystem, running in my workstation right now as I write this review. Also, I understand Cray just announced their intent to include SIMD GPUs in an upcoming supercomputer. So SIMD is making a comeback. But the book provides nearly no instruction for learning SIMD design and coding. I am left to prepare for SIMD using other sources like the vendor-specific NVidia CUDA documentation or perhaps the nascent OpenCL language. SIMD computing has existed long before year 2000, but at the time of the book, SIMD had already fallen out of favor apparently, because Multi Instruction Multiple Data (MIMD) architecture had pretty much taken over the computer scientists' attention, and this book reflects that.Despite the minor quibbles, it is accurate to say the book has a broad coverage of topics within the field. And, at the same time there is the depth and detail such that the reader will develop a feeling of being equipped for a good amount of languages or communication models you will eventually select whatever the job at hand. Like the author says, you can only put so many pages into a single textbook. There are probably whole books out there about any one of the chapters in this book. The bread and butter skill of how to effectively think about concurrent and parallel and distributed systems of nearly all types is presented with clarity and simplicity. This benefit is really the strength of this book. There are many examples built from different models and approaches so you get a sense of what works in what situation. There are also plenty of pseudocode and near-code examples in many languages. Make no mistake, there is also a significant amount of detail and depth of instruction on the essentials such as building correct and high performance and fair critical sections of shared memory. The reader develops a sense for fine grained and coarse grained concurrency; effective control of nondeterministic instruction histories; shared memory versus distributed memory programming; parallel and sequential and distributed and concurrent programming (each is different); concurrent systems-level programming versus concurrent applications-level programming; surveys of important features in different languages including their strengths and weaknesses with regard to the suitability for your system including hardware, network, and software; and parallelizing compilers and language abstractions. You will develop readiness to tackle situations with Ps and Vs (semaphores), monitors, message passing, pthreads, and critical sections.Now, please put aside the tone of the minor criticisms I told you earlier. Andrews book is far more timely than you might have been thinking. Let me demonstrate. Yesterday a brand new language named "Go" was made available to the public by Google. Google appears to be presenting Go as an open-source concurrent-friendly systems programming language. Reading the tutorial for Go, one can see Go provides intrinsics which largely mimic the CSP style of synchronous message passing for interprocess communication and synchronization. Anyone who reads Andrews book will spot the CSP similarity quickly in the new Go language. Moreover the reader will also bring a good preparation on how to use Go's communication model. That's because Andrews' book has provided its readers with good instruction on the synchronous message passing model. Having read Andrews' book, I expect you are better prepared to start programming with what is perhaps one of the more sophisticated and essential parts of the Go language, its intrinsic message passing model. I would suggest that someone who approaches Go without any preparatory knowledge of CSP's guarded synchronous communications model might be at risk of getting mired down in confusion for a while. Go supports concurrency, yet you will not find multithreading and forks and joins in it if I'm understanding it correctly. Those won't be seen explicitly because with message passing those are absent. Perhaps multithreading is the only concurrent technology that some programmers have used, especially if C and pthreads or java or C#.net are among your other area of expertise. My suggestion is to pick up a copy of Andrews book if you want to program some concurrent systems using message passing like Go's. I don't expect a vendor's language tutorial is going to have the time and space to provide all the educational tools, such as a comparitive analysis in different application situations. The book explains the comm and synch model in the context of several different examples; and further, you would want to know where this communication and synchronization model is efficient and simple to code, versus where it is not so simple at times, and this is illustrated as well. Asynchronous message passing code using MPI can be simpler than synchronous code (seen in Go or CSP) depending on the application at hand. Also, maybe you can use CSP to model your Go application. That's because CSP is not just a programming language; it's also a modeling tool which may be useful for Go algorithm design work.The book was (relatively) easy to read independently compared to some other books I've purchased on similar subjects. The worse ones lay dormant on my shelf. My personal copy of the Andrews book has begun showing some wear and real usage... and that's a good thing. The author obviously knows a lot on this subject, yet he still is able to conjure the sympathy and patience for the beginner. He tends to provide the details that you cannot take for granted in the background of a nonexpert (future expert). Some other books by contrast seem to make too-big logical jumps, or use a terseness that leaves you shaking your head and rereading the same sentences over and over like something was left out. It is apparent that more than just a computer scientist, Andrews is also a teacher of knowledge. Independent students can read his book effectively because getting stuck and frustrated is rare, an important boon in the absence of an expert instructor.As Andrews said up front, while his book is broad and has also some depth where it's needed, it is still not the only book you will need. This is not much of a problem in the big picture, especially with the web. I am finding good satisfaction in having studied Andrews' broad introductory book closely. Moving ahead from there, it was useful to study complementary materials like the UC Berkely ParLab short course lecture videos (free), Prof. Demmel's CS267 Apps of Parallel Computers course lecture videos (also free), the linear algebra and calculus math material available on free video at MIT's Opencourseware site, and by reading the more narrowly-scoped but interesting textbook Patterns of Parallel Programming by Mattson et al.Overall, Prof. Andrews' book is a strong basis for learning the principles of design and programming of parallel, concurrent, and distributed systems.
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