大规模并行处理器程序设计
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天津武清
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作者(美)柯克 等著
出版社清华大学出版社
ISBN9787302229735
出版时间2010-07
版次1
装帧平装
开本16开
纸张胶版纸
页数258页
定价36元
上书时间2024-04-23
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基本信息
书名:大规模并行处理器程序设计
定价：36.00元
作者:(美)柯克 等著
出版社：清华大学出版社
出版日期：2010-07-01
ISBN：9787302229735
字数：
页码：258
版次：1
装帧：平装
开本：16开
商品重量：
编辑推荐
《大规模并行处理器程序设计(影印版)》特点：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CLJDA编程模式和OpellCL来获得高性能和高可靠性。
内容提要
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，直到终实现实际且高效的并行程序。 本书特点 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。
目录
PrefaceAcknowledgmentsDedicationCHAPTER 1 INTRODUCTION 1.1 GPUs as Parallel Computers 1.2 Architecture of a Modern GPU 1.3 Why More Speed or Parallelism? 1.4 Parallel Programming Languages and Models 1.5 Overarching Goals 1.6 Organization of the BookCHAPTER 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 TrendsCHAPTER 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 APlCHAPTER 4 CUDA THREADS 4.1 CUDA Thread Organization 4.2 Using b]ockldx and threadIdx 4.3 Synchronization and Transparent Scalability 4.4 Thread Assignment 4.5 Thread Scheduling and Latency Tolerance 4.6 Summary 4.7 ExercisesCHAPTER 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 ExercisesCHAPTER 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 ExercisesCHAPTER 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 ExercisesCHAPTER 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 ExercisesCHAPTER 9 APPLICATION CASE STUDY: MOLECULAR VISUALIZATION AND ANALYSISCHAPTER 10 PARALLEL PROGRAMMING AND COMPUTATIONAL THINKINGCHAPTER 11 A BRIEF INTRODUCTION TO OPENCLTMCHAPTER 12 CONCLUSION AND'FuTuRE OUTLOOKAPPENDIX A MATRIX MULTIPLICATION HOST-ONLY VERSION SOURCE CODEAPPENDIX B GPU COMPUTE CAPABILITIESIndex
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