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

Content preview from CUDA Programming

Preface

Over the past five years there has been a revolution in computing brought about by a company that for successive years has emerged as one of the premier gaming hardware manufacturers—NVIDIA. With the introduction of the CUDA (Compute Unified Device Architecture) programming language, for the first time these hugely powerful graphics coprocessors could be used by everyday C programmers to offload computationally expensive work. From the embedded device industry, to home users, to supercomputers, everything has changed as a result of this.

One of the major changes in the computer software industry has been the move from serial programming to parallel programming. Here, CUDA has produced great advances. The graphics processor unit (GPU) ...