book

CUDA Programming

by Shane Cook

December 2012

Intermediate to advanced

600 pages

18h 19m

English

Morgan Kaufmann

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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

Overview

If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to new hardware, and solving common problems.

Comprehensive introduction to parallel programming with CUDA, for readers new to both
Detailed instructions help readers optimize the CUDA software development kit
Practical techniques illustrate working with memory, threads, algorithms, resources, and more
Covers CUDA on multiple hardware platforms: Mac, Linux and Windows with several NVIDIA chipsets
Each chapter includes exercises to test reader knowledge