CUDA Programming by Cook, Shane -- Read -- Imperial Library of Trantor

Index

Cover image Title page Table of Contents Copyright Preface Chapter 1. A Short History of Supercomputing

Introduction Von Neumann Architecture Cray Connection Machine Cell Processor Multinode Computing The Early Days of Gpgpu Coding The Death of the Single-Core Solution Nvidia and Cuda Gpu Hardware Alternatives to Cuda Conclusion

Chapter 2. Understanding Parallelism with GPUs

Introduction Traditional Serial Code Serial/Parallel Problems Concurrency Types of Parallelism Flynn’s Taxonomy Some Common Parallel Patterns Conclusion

Chapter 3. CUDA Hardware Overview

PC Architecture GPU Hardware CPUs and GPUs Compute Levels

Chapter 4. Setting Up CUDA

Introduction Installing the Sdk Under Windows Visual Studio Linux Mac Installing a Debugger Compilation Model Error Handling Conclusion

Chapter 5. Grids, Blocks, and Threads

What it all Means Threads Blocks Grids Warps Block Scheduling A Practical Example—Histograms Conclusion

Chapter 6. Memory Handling with CUDA

Introduction Caches Register Usage Shared Memory Constant Memory Global Memory Texture Memory Conclusion

Chapter 7. Using CUDA in Practice

Introduction Serial and Parallel Code Processing Datasets Profiling An Example Using AES Conclusion References

Chapter 8. Multi-CPU and Multi-GPU Solutions

Introduction Locality Multi-CPU Systems Multi-GPU Systems Algorithms on Multiple GPUS Which GPU? Single-Node Systems Streams Multiple-Node Systems Conclusion

Chapter 9. Optimizing Your Application

Strategy 1: Parallel/Serial GPU/CPU Problem Breakdown Strategy 2: Memory Considerations Strategy 3: Transfers Strategy 4: Thread Usage, Calculations, and Divergence Strategy 5: Algorithms Strategy 6: Resource Contentions Strategy 7: Self-Tuning Applications Conclusion

Chapter 10. Libraries and SDK

Introduction Libraries CUDA Computing SDK Directive-Based Programming Writing Your Own Kernels Conclusion

Chapter 11. Designing GPU-Based Systems

Introduction CPU Processor GPU Device PCI-E Bus GeForce cards CPU Memory Air Cooling Liquid Cooling Desktop Cases and Motherboards Mass Storage Power Considerations Operating Systems Conclusion

Chapter 12. Common Problems, Causes, and Solutions

Introduction Errors With CUDA Directives Parallel Programming Issues Algorithmic Issues Finding and Avoiding Errors Developing for Future GPUs Further Resources Conclusion References

Index

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