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Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors discusses 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. This book describes computational thinking techniques that will enable students to think about problems in ways that are amenable to high-performance parallel computing. It utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for massively parallel environments. Studies learn how to achieve both high-performance and high-reliability using the CUDA programming model as well as OpenCL. This book is recommended for advanced students, software engineers, programmers, and hardware engineers.
280 pages, Paperback
First published January 1, 2010
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Displaying 1 - 17 of 17 reviews
June 2, 2024
This is very approachable textbook, albeit being tad too verbose. The book structure is almost perfect: starts with hardware level of GPUs with explanation of their drawbacks and benefits, then dives into main processing patterns that are driven by this kind of architecture and finally lays out all popular processing algorithms and applications. As far as intro to GPUs and parallel processing - this book is pretty good.
4 stars -being a textbook it's very verbose, if you read it you can't go away without understanding of what is being said, but the layout does not encourage you to skip parts. If you have basic understanding how parallel sorting works, but just want to see the gpu specific part - tough luck. On the other hand, if you just want to get basic understanding of how parallel sort works - you still have to read the whole chapter with all the intricate details.
The book expects you to consume chapters as a whole and read chapters sequentially.
Another thing that bothered me is that principles of GPU parallel processing are pretty much the same as in distributed systems, but the books doesn't care about such generalizations, and everything is presented as GPU specific problems and solution. This felt off.
Accompanying lectures on youtube:
4 stars -being a textbook it's very verbose, if you read it you can't go away without understanding of what is being said, but the layout does not encourage you to skip parts. If you have basic understanding how parallel sorting works, but just want to see the gpu specific part - tough luck. On the other hand, if you just want to get basic understanding of how parallel sort works - you still have to read the whole chapter with all the intricate details.
The book expects you to consume chapters as a whole and read chapters sequentially.
Another thing that bothered me is that principles of GPU parallel processing are pretty much the same as in distributed systems, but the books doesn't care about such generalizations, and everything is presented as GPU specific problems and solution. This felt off.
Accompanying lectures on youtube:
June 7, 2022
Good intro to parallel programming but a bit wordy.
December 12, 2018
Written in C, good language but only for academics. No set up to practice and creating a feedback cycle. If I want to learn something new by myself, this is not the book to use. Most data science jobs do not ask for C. GPU is useful, but one needs to make money, too.
December 18, 2018
A much better resource than the first edition! It is still rather NVIDIA-centric, but it does spend some time on higher-level and platform-independent APIs as well. I do wish the editors had handled all the color references to black-and-white illustrations better, though.
May 17, 2018
Not a bad introductory book, (if perhaps excessively NVIDIA-centric), but the first edition (which is what I read) is showing its age in 2018.
May 12, 2024
read chapters 3/4, CUDA matmul finally clicked
January 4, 2025
programming gpu's
August 25, 2012
A somewhat light overview of nVidia's GPU architecture and CUDA programming model.
The explanation of GPU architecture was clear if somewhat vague. Given that the whole book was focused on the GT200 series, and they referenced the GTX 295 specifically, I wish they had given exact latency numbers for the memory hierarchy and other hardware features as motivation for why you would do all the backflips necessary to fit your problem effectively into the cuda model. There are a few log-log graphs in the book to show you just how much speed a naive algorithm will give up but they could have done better.
The chapter on floating point was unnecessary, I felt. There was a brief discussion at the end of it about how sorting can affect accuracy due to fixed precision but hardly gave enough detail for the programmer to use effectively.
It has an appendix mapping the CUDA primitives and organization you've learned to their OpenCL equivalents.
The explanation of GPU architecture was clear if somewhat vague. Given that the whole book was focused on the GT200 series, and they referenced the GTX 295 specifically, I wish they had given exact latency numbers for the memory hierarchy and other hardware features as motivation for why you would do all the backflips necessary to fit your problem effectively into the cuda model. There are a few log-log graphs in the book to show you just how much speed a naive algorithm will give up but they could have done better.
The chapter on floating point was unnecessary, I felt. There was a brief discussion at the end of it about how sorting can affect accuracy due to fixed precision but hardly gave enough detail for the programmer to use effectively.
It has an appendix mapping the CUDA primitives and organization you've learned to their OpenCL equivalents.
November 19, 2010
This book provides enough information to start develop programs using CUDA technology. Besides CUDA specific information, it explains how to design algorithms for massively parallel hardware, how to optimize performance, and provides detailed description of two tasks, implemented on GPU. There is also a chapter, that provides short overview of OpenCL technology
September 12, 2024
The book is good and thorough. It is mainly focussed on CUDA, but it is generic on the algorithms approach. But the style doesn't suit my learning style. It defines things, but (in my case) doesn't do a proper job in setting up the concepts (both hardware and algorithms) in a step by step way.
Probably a book for somebody with more knowledge, not an introductory book.
Probably a book for somebody with more knowledge, not an introductory book.
March 10, 2010
Useful explanations of how CUDA programming works. Nice to see more examples and depth than the software documentation. Would like to have seen mention of using the texture memory and associated functions, which were entirely absent.
November 21, 2012
Taught be some about the different memory model issues that can arrise with CUDA. This book asn't put together well. Most of the chapters seemed to spend a couple pages setting the stage as if they all originally stood alone.
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November 21, 2013commetnt
July 21, 2015
Reading the book with the offered course on Coursera will be a very good start. It is written in a very simple way and good for beginners. little bit general but good enough.
December 31, 2024
Good intro to GPU programming. Teaches how regular c program can be changed to take advantage of these power houses using CUDA. 👍
January 15, 2025
Read all of the fundamental chapters and select chapters on tuning for particular algorithms. Very well written with clear exposition. Would recommend to learn GPU programming
May 28, 2015
Great book for beginners.
Displaying 1 - 17 of 17 reviews
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