A Hands-on Approach To order this title, and for more information, click here
By David Kirk, Chief Scientist, NVIDIA Wen-mei Hwu, Professor, University of Illinois
Description Multi-core processors are no longer the future of computing-they are the present day reality. A typical mass-produced CPU features
multiple processor cores, while a GPU (Graphics Processing Unit) may have hundreds or even thousands of cores. With the rise of multi-core
architectures has come the need to teach advanced programmers a new and essential skill: how to program massively parallel processors. Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts
of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies
demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs.
Contents Chapter 1: Introduction GPUs as Parallel Computers Architecture of a Modern GPU Why More Speed or Parallelism? Parallel Programming Languages and Models Overarching
Goals Organization of the Book Chapter 2: History of GPU Computing 2.1. Evolution
of Graphics Pipelines The Era of Fixed Function Graphics Pipeline Evolution of Programmable Real-Time Graphics Unified
Graphics and Computing Processors 2.2. GPGPU: an Intermediate Step Scalable GPUs Recent Developments 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 Function Declarations Kernel Launch Predefined Variables Runtime API Chapter 4: CUDA Threads 4.1.
CUDA Thread Organization 4.2. More on BlockIdx and ThreadIdx 4.3. Synchronization and Transparent Scalability 4.4.
Thread Assignment 4.5. Thread Scheduling and Latency Tolerance 4.6. Summary Chapter 5: CUDA 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 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 Chapter 7: Floating-Point
Considerations 7.1. Floating-Point Format Normalized representation of M 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 Chapter 8: Application Case Study I - Advanced MRI Reconstruction 8.1. Application Background 8.2.
Iterative Reconstruction 8.3. Computing F H d Step 1: Determine the Kernel Parallelism Structure Step 2:
Getting Around the Memory Bandwidth Limitation Step 3: Use Hardware Trigonometry Functions Step 4: Experimental Performance
Testing 8.4. Final Evaluation Chapter 9: Application Case Study II - 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 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 Chapter 11: A Brief Introduction to OpenCL ? 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 Chapter 12: Conclusion and Future Outlook 12.1. Goals Revisited 12.2.
Memory Architecture Evolution 12.3. Kernel Execution Control Evolution 12.4. Core Performance 12.5. Programming
Environment 12.6. A Bright Outlook Appendix A: Matrix Multiplication Example Code Appendix B: Speed and feed of
current generation CUDA devices
Books and book related electronic products are priced in US dollars (USD), euro (EUR), and Great Britain Pounds (GBP). USD prices apply to the Americas and Asia Pacific. EUR prices apply in Europe and the Middle East. GBP prices apply to the UK and all other countries.
Home | Elsevier Sites | Privacy Policy | Terms and Conditions | Feedback | Site Map | A Reed Elsevier Company