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