book

CUDA Programming

by Shane Cook

December 2012

Intermediate to advanced

600 pages

18h 19m

English

Morgan Kaufmann

Read now

Unlock full access

IntroductionVon Neumann ArchitectureCrayConnection MachineCell ProcessorMultinode ComputingThe Early Days of Gpgpu CodingThe Death of the Single-Core SolutionNvidia and CudaGpu HardwareAlternatives to CudaConclusion
IntroductionTraditional Serial CodeSerial/Parallel ProblemsConcurrencyTypes of ParallelismFlynn’s TaxonomySome Common Parallel PatternsConclusion
PC ArchitectureGPU HardwareCPUs and GPUsCompute Levels
IntroductionInstalling the Sdk Under WindowsVisual StudioLinuxMacInstalling a DebuggerCompilation ModelError HandlingConclusion
What it all MeansThreadsBlocksGridsWarpsBlock SchedulingA Practical Example—HistogramsConclusion

IntroductionCachesRegister UsageShared MemoryConstant Memory
Texture MemoryConclusion
IntroductionSerial and Parallel CodeProcessing DatasetsProfilingAn Example Using AESConclusionReferences
IntroductionLocalityMulti-CPU SystemsMulti-GPU SystemsAlgorithms on Multiple GPUSWhich GPU?Single-Node SystemsStreamsMultiple-Node SystemsConclusion
Strategy 1: Parallel/Serial GPU/CPU Problem BreakdownStrategy 2: Memory ConsiderationsStrategy 3: TransfersStrategy 4: Thread Usage, Calculations, and Divergence
Strategy 6: Resource ContentionsStrategy 7: Self-Tuning ApplicationsConclusion
IntroductionLibrariesCUDA Computing SDKDirective-Based ProgrammingWriting Your Own KernelsConclusion
IntroductionCPU ProcessorGPU DevicePCI-E BusGeForce cardsCPU MemoryAir CoolingLiquid CoolingDesktop Cases and MotherboardsMass StoragePower ConsiderationsOperating SystemsConclusion
IntroductionErrors With CUDA DirectivesParallel Programming IssuesAlgorithmic IssuesFinding and Avoiding ErrorsDeveloping for Future GPUsFurther ResourcesConclusionReferences

Content preview from CUDA Programming

Chapter 4 Setting Up CUDA

Introduction

This chapter is here for anyone who is completely new to CUDA. We look at how to install CUDA on the various OSs, what tools you can use, and how CUDA compiles. Finally, we look at how to have the API help you identify the coding and API errors everyone makes.

CUDA is supported on three major OSs: Windows, Mac, and Linux. By far the easiest platform to use and learn CUDA with is the OS you are most familiar with using for programming development. For an absolute beginner, the Windows OS in conjunction with Microsoft Visual C++ is likely to be the best choice. Both the Windows and Mac installations are fairly much point and click. Both provide fairly standard integrated development environments that work ...