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Programming Massively Parallel Processors: A Hands-on Approach
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Programming Massively Parallel Processors: A Hands-on Approach shows both students and professionals alike the basic concepts of parallel programming and GPU architecture.
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- Programming Massively Parallel Processors: A Hands-on Approach shows both students and professionals alike the basic concepts of parallel programming and GPU architecture. Concise, intuitive, and practical, it is based on years of road-testing in the authors' own parallel computing courses. Various techniques for constructing and optimizing parallel programs are explored in detail, while case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. The new edition includes updated coverage of CUDA, including the newer libraries such as CuDNN. New chapters on frequently used parallel patterns have been added, and case studies have been updated to reflect current industry practices. Parallel Patterns Introduces new chapters on frequently used parallel patterns (stencil, reduction, sorting) and major improvements to previous chapters (convolution, histogram, sparse matrices, graph traversal, deep learning)Ampere Includes a new chapter focused on GPU architecture and draws examples from recent architecture generations, including Ampere Systematic Approach Incorporates major improvements to abstract discussions of problem decomposition strategies and performance considerations, with a new optimization checklist
| Publisher | Morgan Kaufmann |
| Publication date | August 18, 2022 |
| Edition | 4th |
| Language | English |
| Print length | 580 pages |
| ISBN-10 | 0323912311 |
| ISBN-13 | 978-0323912310 |
| Item Weight | 1.95 pounds (880 grams) |
| Dimensions | 7.25 x 0.75 x 9 inches (18.4 x 1.9 x 22.9 cm) |
Who Should Buy?
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Students in Academia
Ideal for students studying parallel computing, offering hands-on exercises with CUDA programming for practical learning.
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Professional Developers
Helpful for experienced developers looking to enhance skills in parallel programming for high-performance applications.
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Researchers in HPC
Useful for researchers focusing on high-performance computing, providing insights into programming with massively parallel processors.
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Beginners in Programming
Not suitable for beginners without prior programming knowledge, as the content is advanced and assumes familiarity.
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Microprocessor Design Editorial Review
Programming Massively Parallel Processors: A Hands-on Approach is an essential read for anyone looking to master CUDA programming. The book is published by Morgan Kaufmann, and its fourth edition provides 580 pages rich in content, guiding readers through the basics of CUDA and offering case studies to solidify understanding. Readers have praised the clear explanations, especially in the foundational chapters, making complex concepts more accessible. While it comprehensively covers programming abstractions and hardware differences, some users noted the absence of detailed coverage on CUDA C++, tensor cores, and empirical performance analysis. Despite these minor gaps, it's considered a valuable resource for both teaching and practical application.
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Pros
- Clear explanations help grasp complex CUDA concepts
- Practical case studies for real-world kernel optimization
- Structurally well-organized for teaching purposes
- Deep dives into parallel programming techniques
- Excellent foundational resource for beginners
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Features & Benefits
- The book explains the basic concepts of parallel programming and GPU architecture in a concise, intuitive, and practical manner.
- It explores various techniques for constructing and optimizing parallel programs with case studies.
- New chapters on frequently used parallel patterns have been added and case studies updated to reflect current industry practices.
- It includes updated coverage of CUDA, including newer libraries such as CuDNN.
- A new chapter on GPU architecture draws examples from recent architecture generations, including Ampere.
- It incorporates major improvements to abstract discussions of problem decomposition strategies and performance considerations with a new optimization checklist.
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