에너지경제연구원 전자도서관

로그인

에너지경제연구원 전자도서관

자료검색

  1. 메인
  2. 자료검색
  3. 통합검색

통합검색

단행본

Programming massively prallel pocessors: a hands-on approach

판사항
Fourth edition
발행사항
Cambridge, MA :bMorgan Kaufmann, 2022
형태사항
xxviii, 551pages : illustrations, graph ; 24cm
서지주기
Includes bibliographical references and index
소장정보
위치등록번호청구기호 / 출력상태반납예정일
지금 이용 불가 (1)
자료실E208046대출중2025.07.07
지금 이용 불가 (1)
  • 등록번호
    E208046
    상태/반납예정일
    대출중
    2025.07.07
    위치/청구기호(출력)
    자료실
책 소개

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.



Feature

  • 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


목차
Foreword Preface Acknowledgments CHAPTER 1. Introduction PART Ⅰ Fundamental Concepts CHAPTER 2. Heterogeneous data parallel computing CHAPTER 3. Multidimensional grids and data CHAPTER 4. Compute architecture and scheduling CHAPTER 5. Memory architecture and data locality CHAPTER 6. Performance considerations PART Ⅱ Parallel Patterns CHAPTER 7. Convolution CHAPTER 8. Stencil CHAPTER 9. Parallel histogram CHAPTER 10. Reduction CHAPTER 11. Prefix sum(scan) CHAPTER 12. Merge PART Ⅲ Advanced Patterns and Applications CHAPTER 13. Sorting CHAPTER 14. Sparse matrix computation CHAPTER 15. Graph traversal CHAPTER 16. Deep learning CHAPTER 17. Iterative magnetic resonacne imaging reconstruction CHAPTER 18. Electrostatic potential map CHAPTER 19. Parallel programming and computational thinking PART Ⅳ Advanced Practices CHAPTER 20. Programming a heterogeneous computing cluster CHAPTER 21. CUDA dynamic parallelism CHAPTER 22. Advanced practices and future evolution CHAPTER 23. Conclusion and outlook Appendix A: Numerical considerations Index