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Programming Massively Parallel Processors: A Hands-on Approach
출판사 : Elsevier
저 자 : David B. Kirk, Wen-mei W. Hwu
ISBN : 9780123814722
발행일 : 2010-2
도서종류 : 외국도서
발행언어 : 영어
페이지수 : 280
판매가격 : 35,000원
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   Programming Massively Parallel Processors: A Hands-on Approach 목차
Table of Contents
Chapter 1: Introduction

Chapter 2: History of GPU Computing

Chapter 3: Introduction to CUDA

Chapter 4: CUDA Threads

Chapter 5: CUDA Memories

Chapter 6: Performance Considerations

Chapter 7: Floating-Point Considerations

Chapter 8: Application Case Study I - Advanced MRI Reconstruction

Chapter 9: Application Case Study II - Molecular Visualization and Analysis

Chapter 10: Parallel Programming and Computational Thinking

Chapter 11: A Brief Introduction to OpenCL ™

Chapter 12: Conclusion and Future Outlook

Appendix A: Matrix Multiplication Example Code

Appendix B: Speed and feed of current generation CUDA devices

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From the Publisher

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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