Programming Massively Parallel Processors: A Hands-on Approach (Google eBook)

Front Cover
Elsevier, Feb 22, 2010 - Computers - 280 pages
3 Reviews

Multi-core processors are no longer the future of computing-they are the present day reality. A typical mass-produced CPU features multiple processor cores, while a GPU (Graphics Processing Unit) may have hundreds or even thousands of cores. With the rise of multi-core architectures has come the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.

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.



  • Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing.
  • Utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for massively parallel environments.
  • Shows you how to achieve both high-performance and high-reliability using the CUDA programming model as well as OpenCL.
  

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LibraryThing Review

User Review  - jcopenha - LibraryThing

A great introduction to programming on GPUs but you should probably buy another book with it. This is a great read for the concepts and just enough code so you have an idea of what is going on. The performance considerations discussed in the various chapters are also great. Read full review

Review: Programming Massively Parallel Processors: A Hands-On Approach

User Review  - Phillip Nordwall - Goodreads

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. Read full review

Contents

Introduction
1
History of GPU Computing
21
Introduction to CUDA
39
CUDA Threads
59
CUDA Memories
77
Performance Considerations
95
Floating Point Considerations
125
Application Case Study Advanced MRI Reconstruction
141
Application Case Study Molecular Visualization and Analysis
173
Parallel Programming and Computational Thinking
191
A Brief Introduction to OpenCL
205
Conclusion and Future Outlook
221
Matrix Multiplication HostOnly Version Source Code
233
GPU Compute Capabilities
245
Index
251
Copyright

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