Advances in GPU Research and Practice focuses on research and practices in GPU based systems. The topics treated cover a range of issues, ranging from hardware and architectural issues, to high level issues, such as application systems, parallel programming, middleware, and power and energy issues.
Divided into six parts, this edited volume provides the latest research on GPU computing. Part I: Architectural Solutions focuses on the architectural topics that improve on performance of GPUs, Part II: System Software discusses OS, compilers, libraries, programming environment, languages, and paradigms that are proposed and analyzed to help and support GPU programmers. Part III: Power and Reliability Issues covers different aspects of energy, power, and reliability concerns in GPUs. Part IV: Performance Analysis illustrates mathematical and analytical techniques to predict different performance metrics in GPUs. Part V: Algorithms presents how to design efficient algorithms and analyze their complexity for GPUs. Part VI: Applications and Related Topics provides use cases and examples of how GPUs are used across many sectors.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Dedication - p. v 6. List of Contributors - p. xix 7. Preface - p. xxiii 8. Acknowledgments - p. xxxi 9. Part 1: Programming and Tools - p. xxxi 10. Chapter 1: Formal analysis techniques for reliable GPU programming: current solutions and call to action - p. xxxi 11. Chapter 2: SnuCL: A unified OpenCL framework for heterogeneous clusters - p. 22 12. Chapter 3: Thread communication and synchronization on massively parallel GPUs - p. 55 13. Chapter 4: Software-level task scheduling on GPUs - p. 81 14. Chapter 5: Data placement on GPUs - p. 103 15. Part 2: Algorithms and Applications - p. 123 16. Chapter 6: Biological sequence analysis on GPU - p. 123 17. Chapter 7: Graph algorithms on GPUs - p. 162 18. Chapter 8: GPU alignment of two and three sequences - p. 198 19. Chapter 9: Augmented Block Cimmino Distributed Algorithm for solving tridiagonal systems on GPU - p. 232 20. Chapter 10: GPU computing applied to linear and mixed-integer programming - p. 246 21. Chapter 11: GPU-accelerated shortest paths computations for planar graphs - p. 271 22. Chapter 12: GPU sorting algorithms - p. 306 23. Chapter 13: MPC: An effective floating-point compression algorithm for GPUs - p. 326 24. Chapter 14: Adaptive sparse matrix representation for efficient matrix-vector multiplication - p. 347 25. Part 3: Architecture and Performance - p. 369 26. Chapter 15: A framework for accelerating bottlenecks in GPU execution with assist warps - p. 369 27. Chapter 16: Accelerating GPU accelerators through neural algorithmic transformation - p. 415 28. Chapter 17: The need for heterogeneous network-on-chip architectures with GPGPUs: A case study with photonic interconnects - p. 448 29. Chapter 18: Accurately modeling GPGPU frequency scaling with the CRISP performance model - p. 469 30. Part 4: Power and Reliability - p. 505 31. Chapter 19: Energy and power considerations of GPUs - p. 505 32. Chapter 20: Architecting the last-level cache for GPUs using STT-MRAM nonvolatile memory - p. 541 33. Chapter 21: Power management of mobile GPUs - p. 580 34. Chapter 22: Advances in GPU reliability research - p. 582 35. Chapter 23: Addressing hardware reliability challenges in general-purpose GPUs - p. 647 36. Author Index - p. 707 37. Subject Index - p. 727
Advances in GPU Research and Practice focuses on research and practices in GPU based systems. The topics treated cover a range of issues, ranging from hardware and architectural issues, to high level issues, such as application systems, parallel programming, middleware, and power and energy issues.
Divided into six parts, this edited volume provides the latest research on GPU computing. Part I: Architectural Solutions focuses on the architectural topics that improve on performance of GPUs, Part II: System Software discusses OS, compilers, libraries, programming environment, languages, and paradigms that are proposed and analyzed to help and support GPU programmers. Part III: Power and Reliability Issues covers different aspects of energy, power, and reliability concerns in GPUs. Part IV: Performance Analysis illustrates mathematical and analytical techniques to predict different performance metrics in GPUs. Part V: Algorithms presents how to design efficient algorithms and analyze their complexity for GPUs. Part VI: Applications and Related Topics provides use cases and examples of how GPUs are used across many sectors.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Dedication - p. v 6. List of Contributors - p. xix 7. Preface - p. xxiii 8. Acknowledgments - p. xxxi 9. Part 1: Programming and Tools - p. xxxi 10. Chapter 1: Formal analysis techniques for reliable GPU programming: current solutions and call to action - p. xxxi 11. Chapter 2: SnuCL: A unified OpenCL framework for heterogeneous clusters - p. 22 12. Chapter 3: Thread communication and synchronization on massively parallel GPUs - p. 55 13. Chapter 4: Software-level task scheduling on GPUs - p. 81 14. Chapter 5: Data placement on GPUs - p. 103 15. Part 2: Algorithms and Applications - p. 123 16. Chapter 6: Biological sequence analysis on GPU - p. 123 17. Chapter 7: Graph algorithms on GPUs - p. 162 18. Chapter 8: GPU alignment of two and three sequences - p. 198 19. Chapter 9: Augmented Block Cimmino Distributed Algorithm for solving tridiagonal systems on GPU - p. 232 20. Chapter 10: GPU computing applied to linear and mixed-integer programming - p. 246 21. Chapter 11: GPU-accelerated shortest paths computations for planar graphs - p. 271 22. Chapter 12: GPU sorting algorithms - p. 306 23. Chapter 13: MPC: An effective floating-point compression algorithm for GPUs - p. 326 24. Chapter 14: Adaptive sparse matrix representation for efficient matrix-vector multiplication - p. 347 25. Part 3: Architecture and Performance - p. 369 26. Chapter 15: A framework for accelerating bottlenecks in GPU execution with assist warps - p. 369 27. Chapter 16: Accelerating GPU accelerators through neural algorithmic transformation - p. 415 28. Chapter 17: The need for heterogeneous network-on-chip architectures with GPGPUs: A case study with photonic interconnects - p. 448 29. Chapter 18: Accurately modeling GPGPU frequency scaling with the CRISP performance model - p. 469 30. Part 4: Power and Reliability - p. 505 31. Chapter 19: Energy and power considerations of GPUs - p. 505 32. Chapter 20: Architecting the last-level cache for GPUs using STT-MRAM nonvolatile memory - p. 541 33. Chapter 21: Power management of mobile GPUs - p. 580 34. Chapter 22: Advances in GPU reliability research - p. 582 35. Chapter 23: Addressing hardware reliability challenges in general-purpose GPUs - p. 647 36. Author Index - p. 707 37. Subject Index - p. 727
