More New and Used
from Private Sellers
Usually Ships in 2-3 Business Days
Starting at $21.65
Questions About This Book?
Why should I rent this book?
Renting is easy, fast, and cheap! Renting from eCampus.com can save you hundreds of dollars compared to the cost of new or used books each semester. At the end of the semester, simply ship the book back to us with a free UPS shipping label! No need to worry about selling it back.
How do rental returns work?
Returning books is as easy as possible. As your rental due date approaches, we will email you several courtesy reminders. When you are ready to return, you can print a free UPS shipping label from our website at any time. Then, just return the book to your UPS driver or any staffed UPS location. You can even use the same box we shipped it in!
What version or edition is this?
This is the 2nd edition with a publication date of 12/14/2012.
What is included with this book?
- The Used copy of this book is not guaranteed to inclue any supplemental materials. Typically, only the book itself is included.
- The Rental copy of this book is not guaranteed to include any supplemental materials. You may receive a brand new copy, but typically, only the book itself.
This best-selling guide to CUDA and GPU parallel programming has been revised with more parallel programming examples, commonly-used libraries, and explanations of the latest tools. With these improvements, the book retains its concise, intuitive, practical approach based on years of road-testing in the authors' own parallel computing courses. 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. Updates in this edition include: New coverage of CUDA 4.0, improved performance, enhanced development tools, increased hardware support, and more Increased coverage of related technology OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism Two new case studies explore the latest applications of CUDA and GPUs for scientific research and high-performance computing