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Programming Massively Parallel Processors: A Hands-on Approach
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Review
"For those interested in the GPU path to parallel enlightenment, this new book from David Kirk and Wen-mei Hwu is a godsend, as it introduces CUDA (tm), a C-like data parallel language, and Tesla(tm), the architecture of the current generation of NVIDIA GPUs. In addition to explaining the language and the architecture, they define the nature of data parallel problems that run well on the heterogeneous CPU-GPU hardware ... This book is a valuable addition to the recently reinvigorated parallel computing literature."Â --David Patterson, Director of The Parallel Computing Research Laboratory and the Pardee Professor of Computer Science, U.C. Berkeley. Co-author of Computer Architecture: A Quantitative Approach "Written by two teaching pioneers, this book is the definitive practical reference on programming massively parallel processors--a true technological gold mine. The hands-on learning included is cutting-edge, yet very readable. This is a most rewarding read for students, engineers, and scientists interested in supercharging computational resources to solve today's and tomorrow's hardest problems."Â --Nicolas Pinto, MIT, NVIDIA Fellow, 2009 "I have always admired Wen-mei Hwu's and David Kirk's ability to turn complex problems into easy-to-comprehend concepts. They have done it again in this book. This joint venture of a passionate teacher and a GPU evangelizer tackles the trade-off between the simple explanation of the concepts and the in-depth analysis of the programming techniques. This is a great book to learn both massive parallel programming and CUDA." --Mateo Valero, Director, Barcelona Supercomputing Center "The use of GPUs is having a big impact in scientific computing. David Kirk and Wen-mei Hwu's new book is an important contribution towards educating our students on the ideas and techniques of programming for massively parallel processors."Â --Mike Giles, Professor of Scientific Computing, University of Oxford "This book is the most comprehensive and authoritative introduction to GPU computing yet. David Kirk and Wen-mei Hwu are the pioneers in this increasingly important field, and their insights are invaluable and fascinating. This book will be the standard reference for years to come."Â --Hanspeter Pfister, Harvard University "This is a vital and much-needed text. GPU programming is growing by leaps and bounds. This new book will be very welcomed and highly useful across inter-disciplinary fields."Â --Shannon Steinfadt, Kent State University "GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors."Â --CNNMoney.com
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From the Back Cover
Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs.
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Product details
Paperback: 514 pages
Publisher: Morgan Kaufmann; 2 edition (December 28, 2012)
Language: English
ISBN-10: 0124159923
ISBN-13: 978-0124159921
Product Dimensions:
7.5 x 1.2 x 9.2 inches
Shipping Weight: 1.8 pounds
Average Customer Review:
3.8 out of 5 stars
23 customer reviews
Amazon Best Sellers Rank:
#269,667 in Books (See Top 100 in Books)
Although not evident from the title, this book is mainly about CUDA, NVIDIA's method for programming their GPUs, but the title is perhaps justified by the existence of chapters on half a dozen or so other packages for programming GPUs, including an important one on using GPUs on MPI lusters. Most of the examples are in C, but with a chapter on FORTRAN. As other reviewers have already mentioned, the book is either excessively wordy or wonderfully detailed, depending on your mode of learning. For me, it was too wordy: there were sometimes two or three pages to explain an algorithm that was immediately clear from the figure. I would add that the authors have a curious view of what readers might know or not know: they give a long explanation of IEEE floating-point formats, which I believe are well known among application programmers, but mention only in passing with no explanation such less well known concepts as atomic operations and Amdahl's law. Various generations of NVIDIA chips are mentioned by code names that are probably not familiar to those of us who just buy a graphics card, although an appendix at the end gives some details. All in all, I found this a very useful introduction to CUDA, and especially to problems of memory access management on GPU boards, which I knew nothing about beforehand. Nonetheless, it was clear that one would need access to the detailed library manuals and spec sheets for the particular chip one was using before undertaking a real coding project, as there seem to be rather significant differences between chips in how one might try to maximize efficient use of the multiple execution units, memory blocks, and different cache sizes on the different chips. I expect that to have life beyond the current generation of chips, an application would need to query the system for these properties and optimize accordingly at run time. The book has some hints on how to do this.
So much effort went into writing this text, but better guidance was needed. True, there are typos as many have said, but not as many as I find in most textbooks and research papers.Before I get started, I have one strong compliment for the text: exercises at the ends of the chapters, where they appear, are very well thought out and get right to the point.The worst part of this book is its index, which gets an unnegotiable F grade from me. So much is missing. Try looking for the gridDim built-in variable; it's not there. blockDim is, but not gridDim. Or look up "constant memory"; that's not there under "memory", "constant", or otherwise. In fact although most of parallel programming is about overcoming memory bottlenecks, the index doesn't say much about memory in general. Indexing books is very hard and is usually left to professional indexers; there really is such a trade. But for this book the authors needed to pay more attention to the end product.The overall arrangement is illogical, starting from an enormous abstraction and then adding detail. Better to begin with something small and concrete which can be understood, and build up.At several points a non-programmer was permitted to do the typing, which is like allowing a pilot to fly while waking out of general anesthesia. See Figure 10.4 at lines 1 and 5.Many concepts are never brought together in some kind of summary that one would ever turn to as a reference, such as special variables. One can also find seeming conflicts that need clarification, such as Table 5.1's "local" memory for automatic array variables that are "stored into the global memory" on the next page. Both turn out to be true, but the presentation is not ideal.References are sloppily missing in certain cases, such as ELLPACK on page 224.At several points the text completely derails with outrageous metaphors. For instance page 85, where the authors try to explain standard queries of an implementation's capabilities by informing us that "most American hotel rooms come with hair dryers". The illustration's purpose is to publish a thicker book that looks "more valuable", but the truth is that any programmer who read to page 85 knows that most libraries have functions for querying their parameters and limits. There's no need to digress into toothpaste in Asia.If you must take a course that is based on this text, things get worse for you: the authors made presentation slides, and you bear the risk that your instructor may use them in the classroom. These are not well thought out from either a clarity or instructional standpoint, and they are very cluttered with confusing details. One example is how tiled matrix multiplication is explained; one slide actually draws three 4x4 matrices with row, column, and matrix name (!!!) labels in every single cell, giving the poor class 3 x (4 x 4) x 3 = 144 extra trivialities to distract them on that slide alone.Another pedagogical problem with their slide example, which the book also mishandles, is that they've chosen to illustrate the use of submatrices by dividing a 4 x 4 problem into a 2 x 2 problem. Although this is the smallest case they could select, the difficulty is that the human brain comprehends these 2 x 2 divisions in terms of "corners" (top left, etc.) instead of columns and rows. The examples should have been 9 x 9 divided into 3 x 3.In fairness, Kirk and Hwu did not have the best of resources available to assemble this book from. nVIDIA's "CUDA C Programming Guide" has no index whatsoever, and the table of contents doesn't seem to have much to do with what one is trying to find a second time. There's a reason nVIDIA doesn't sell print copies of that text on Amazon; people would review it.
the content of the book is excellent but the prose and code are marred by numerous typos - a veritable horde of them. The illustrations in the print book are blurry but the kindle version is a little better. It is a shame that the effort of Drs. Kirk & Hwu are marred by poor editing and production
Like the authors, the book is clear, down to earth, willing to speak truth to power, and if you listen to them (read the book) you will do better science. A serially executing program is a not-too-special case of a massively parallel solution.Code samples and extensive graphics will help you on your transition. The history from an NVIDIA insider is fascinating, and the team used this material to teach people that have since done excelent work.
This book is quite useful in understanding CUDA fundamentals. But, buyer beware. If you are new to CUDA or C programming, you may find this book confusing, as typos run throughout. They are easy to pick out if you know basic CUDA syntax. If you do not, "CUDA by Example" is a better introduction.
I am teaching a Parallel Programming for Master students. The APIs I am teaching are MPI/CUDA/OpenMP these are enough for a student to grasp and appreciate parallel programming.For the CUDA course I have selected this book to start and fill in with material of other books. For instance when this books describes how CUDA maps to multidimension data like an image there should be an example. The best example is the julia example of the book CUDA by example of course modified so as to teach coalescing in the writes and what happens when DIM is not divisible by the number of threads.After two years I will be able to present the material I am teaching. I think this will be helpful.
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