本书介绍了并行计算的思想,使得读者可以把这种问题的思考方式渗透到高性能并行计算中去;介绍了CUDA的使用,CUDA是NVIDIA公司专门为大规模并行环境创建的一种软件开发工具;介绍如何使用CUDA编程模式和OpenCL来获得高性能和高可靠性。
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书名 | 大规模并行处理器程序设计(影印版)/大学计算机教育国外著名教材系列 |
分类 | |
作者 | (美)柯克 |
出版社 | 清华大学出版社 |
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简介 | 编辑推荐 本书介绍了并行计算的思想,使得读者可以把这种问题的思考方式渗透到高性能并行计算中去;介绍了CUDA的使用,CUDA是NVIDIA公司专门为大规模并行环境创建的一种软件开发工具;介绍如何使用CUDA编程模式和OpenCL来获得高性能和高可靠性。 内容推荐 本书介绍了并行程序设计与GPU体系结构的基本概念,并详细探讨了用于构建并行程序的各种技术,用案例演示了并行程序设计的整个开发过程,即从并行计算的思想开始,直到最终实现实际且高效的并行程序。 目录 Preface Acknowledgments Dedication CHAPTER 1 INTRODUCTION 1.1 GPUs as Parallel Computers 1.2 Architecture of a Modem GPU 1.3 Why More Speed or Parallelism? 1.4 Parallel Programming Languages and Models 1.5 0verarching Goals 1.6 Organization of the Book CHAPTER 2 HISTORY OF GPU COMPUTING 2.1 Evolution of Graphics Pipelines 2.1.1 The Era of Fixed-Function Graphics Pipelines 2.1.2 Evolution of Programmable Real-Time Graphics 2.1.3 Unified Graphics and Computing Processors 2.1.4 GPGPU: An Intermediate Step 2.2 GPU Computing 2.2.1 Scalable GPUs 2.2.2 Recent Developments 2.3 Future Trends CHAPTER 3 INTRODUCTION TO CUDA 3.1 Data Parallelism 3.2 CUDA Program Structure 3.3 A Matrix-Matrix Multiplication Example 3.4 Device Memories and Data Transfer 3.5 Kernel Functions and Threading 3.6 Summary 3.6.1 Function declarations 3.6.2 Kernel launch 3.6.3 Predefined variables 3.6.4 Runtime API CHAPTER 4 CUDA THREADS 4.1 CUDA Thread Organization 4.2 blockIdx and threadIdx 4.3 Synchronization and Transparent Scalability 4.4 Thread Assignment 4.5 Thread Scheduling and Latency Tolerance 4.6 Summary 4.7 Exercises CHAPTER 5 CUDATM MEMORIES 5.1 Importance of Memory Access Efficiency 5.2 CUDA Device Memory Types 5.3 A Strategy for Reducing Global Memory Traffic 5.4 Memory as a Limiting Factor to Parallelism 5.5 Summary 5.6 Exercises CHAPTER 6 PERFORMANCE CONSIDERATIONS 6.1 More on Thread Execution 6.2 Global Memory Bandwidth 6.3 Dynamic Partitioning of SM Resources 6.4 Data Prefetching 6.5 Instruction Mix 6.6 Thread Granularity 6.7 Measured Performance and Summary 6.8 Exercises CHAPTER 7 FLOATING POINT CONSIDERATIONS 7.1 Floating-Point Format 7.1.1 Normalized Representation of M 7.1.2 Excess Encoding of E 7.2 Representable Numbers 7.3 Special Bit Patterns and Precision 7.4 Arithmetic Accuracy and Rounding 7.5 Algorithm Considerations 7.6 Summary 7.7 Exercises CHAPTER 8 APPLICATION CASE STUDY: ADVANCED MRI RECONSTRUCTION 8.1 Application Background 8.2 Iterative Reconstruction 8.3 Computing FHd Step 1. Determine the Kernel Parallelism Structure Step 2. Getting Around the Memory Bandwidth Limitation. Step 3. Using Hardware Trigonometry Functions Step 4. Experimental Performance Tuning 8.4 Final Evaluation 8.5 Exercises CHAPTER 9 APPLICATION CASE STUDY: MOLECULAR VISUALIZATION AND ANALYSIS 9.1 Application Background 9.2 A Simple Kernel Implementation 9.3 Instruction Execution Efficiency 9.4 Memory Coalescing 9.5 Additional Performance Comparisons 9.6 Using Multiple GPUs 9.7 Exercises CHAPTER 10 PARALLEL PROGRAMMING AND COMPUTATIONAL THINKING 10.1 Goals of Parallel Programming 10.2 Problem Decomposition 10.3 Algorithm Selection 10.4 Computational Thinking 10.5 Exercises CHAPTER 11 A BRIEF INTRODUCTION TO OPENCLTM 11.1 Background 11.2 Data Parallelism Model 11.3 Device Architecture 11.4 Kernel Functions 11.5 Device Management and Kernel Launch 11.6 Electrostatic Potential Map in OpenCL 11.7 Summary 11.8 Exercises CHAPTER 12 CONCLUSION AND FUTURE OUTLOOK 12.1 Goals Revisited 12.2 Memory Architecture Evolution 12.2.1 Large Virtual and Physical Address Spaces 12.2.2 Unified Device Memory Space 12.2.3 Configurable Caching and Scratch Pad 12.2.4 Enhanced Atomic Operations 12.2.5 Enhanced Global Memory Access 12.3 Kernel Execution Control Evolution 12.3.1 Function Calls within Kernel Functions 12.3.2 Exception Handling in Kernel Functions 12.3.3 Simultaneous Execution of Multiple Kernels 12.3.4 Interruptible Kernels 12.4 Core Performance 12.4.1 Double-Precision Speed 12.4.2 Better Control Flow Efficiency 12.5 Programming Environment 12.6 A Bright Outlook APPENDIX A MATRIX MULTIPLICATION HOST-ONLY VERSION SOURCE CODE A.1 matrixmul.cu A.2 matri mulgol d.cpp A.3 matrixmul, h A.4 assi st. h A.5 Expected Output APPENDIX B GPU COMPUTE CAPABILITIES B.1 GPU Compute Capability Tables B.2 Memory Coalescing Variations Index |
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