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GPU Programming with C++ and CUDA · LearnSpace
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GPU Programming with C++ and CUDA

Курс от Packt
Средний≈ 14.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

In this course, you’ll master GPU programming using C++ and CUDA to significantly enhance your software's performance. By focusing on parallelism, you’ll learn to leverage the full power of GPUs for high-performance computing applications. You will acquire practical knowledge on managing GPU devices, optimizing GPU resource usage, and integrating GPU code with Python to build scalable and efficient applications. This course emphasizes real-world strategies for optimizing performance and building reusable libraries. This course combines fundamental theory with hands-on applications to help you solve complex performance challenges. You'll not only understand the core concepts but also implement them in real-world projects, such as creating libraries for Python integration. Ideal for C++ developers with experience in basic programming concepts, this course will take you through advanced topics, from parallel algorithms to multi-GPU usage. A background in operating systems is recommended for tackling more complex concepts. Based on the book, GPU Programming with C++ and CUDA, by Paulo Motta.

Навыки, которые вы освоите

Performance TuningDevelopment TestingC++ (Programming Language)Computer ArchitecturePerformance TestingComputer ProgrammingPython ProgrammingContainerizationProgramming PrinciplesDocker (Software)C and C++Hardware ArchitectureDebuggingMemory ManagementDevelopment EnvironmentAlgorithmsTest ToolsSoftware-Defined NetworkingSoftware Development ToolsScripting

Программа курса

10 модулей · 76 учебных материалов

01Introduction to Parallel Programming8 материалов

Lesson 1

Introduction - Overview VideoВидеоIntroduction to Parallel Programming - Overview VideoВидеоIntroductionЧтениеWhy Is Parallelism Important?Чтение

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Packt - Course Instructors

Преподаватель курса

GPU Programming with C++ and CUDA
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 14.3 ч

10 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Корейский, Индонезийский, Испанский, Японский

Часть программы вашего университета
An Overview of GPU ArchitectureЧтение
Check In: CPU vs. GPU Sense-MakingDIALOGUE
Memory Management and AccessЧтение
Parallel Programming FundamentalsЗадание
02Setting Up Your Development Environment5 материалов

Lesson 1

Setting Up Your Development Environment - Overview VideoВидеоIntroductionЧтениеDocker at a GlanceЧтениеReadying Our Development EnvironmentЧтениеDocker and CUDA Setup FundamentalsЗадание
03Hello CUDA6 материалов

Lesson 1

Hello CUDA - Overview VideoВидеоIntroductionЧтениеA First Running ProgramЧтениеConsulting DevicesЧтениеVS Code ConfigurationЧтениеIntroduction to CUDA and Dev ContainersЗадание
04Hello Again, but in Parallel8 материалов

Lesson 1

Hello Again, but in Parallel - Overview VideoВидеоIntroductionЧтениеNot-So-Parallel Prime Number VerificationЧтениеA Kernel to Test for Prime NumbersЧтениеCheck In: CPU vs. GPU Sense-MakingDIALOGUEHow to Measure Execution Time on the GPUЧтениеVector AdditionЧтениеParallel Computing FundamentalsЗадание
05A Closer Look into the World of GPUs8 материалов

Lesson 1

A Closer Look into the World of GPUs - Overview VideoВидеоIntroductionЧтениеPutting It All TogetherЧтениеAsynchronous Data TransfersЧтениеPractice: Design an Optimized GPU Execution PlanDIALOGUEParallelizing with StreamsЧтениеFollowing the EventsЧтениеExploring GPU Architecture and Optimization TechniquesЗадание
06Parallel Algorithms with CUDA11 материалов

Lesson 1

Parallel Algorithms with CUDA - Overview VideoВидеоIntroductionЧтениеUnderstanding How to Spot and Exploit ParallelismЧтениеBalancing the WorkloadsЧтениеComputing Matrix Addition and MultiplicationЧтениеPractice: Strategize for Parallel Algorithms in CUDADIALOGUECalculating Numerical IntegralsЧтениеReducing from ManyЧтениеSorting DataЧтениеProcessing Sensor Data with a ConvolutionЧтениеData Management and Parallel ExecutionЗадание
07Performance Strategies8 материалов

Lesson 1

Performance Strategies - Overview VideoВидеоIntroductionЧтениеProfiling with NVIDIA Nsight ComputeЧтениеOptimizing to Speed Up Our CodeЧтениеPractice: Analyze a Profiler ReportDIALOGUEUsing the Release ConfigurationЧтениеUsing Loop Unroll for Further ImprovementsЧтениеPerformance Strategies in Software DevelopmentЗадание
08Overlaying Multiple Operations7 материалов

Lesson 1

Overlaying Multiple Operations - Overview VideoВидеоIntroductionЧтениеUsing CUDA Streams to Overlay OperationsЧтениеPractice: Design a CUDA Optimization PlanDIALOGUEMeasuring Our LimitsЧтениеRunning Multiple GPUs TogetherЧтениеMulti-GPU and CUDA Stream FundamentalsЗадание
09Exposing Your Code to Python7 материалов

Lesson 1

Exposing Your Code to Python - Overview VideoВидеоIntroductionЧтениеCreating the C++ LibraryЧтениеUsing CtypesЧтениеPractice: Choosing the Right C++/Python BridgeDIALOGUEPassing NumPy Arrays to Your LibraryЧтениеIntegrating C++ with Python for High-Performance ComputingЗадание
10Exploring Existing GPU Models8 материалов

Lesson 1

Exploring Existing GPU Models - Overview VideoВидеоExploring Existing GPU ModelsЧтениеUsing Thrust to Write GPU CodeЧтениеMoving Sequential Code to the GPUЧтениеPractice: Designing a CPU/GPU Processing PipelineDIALOGUETesting Your Code with GTest and PytestЧтениеUsing Pytest with Our CodeЧтениеGPU Programming FundamentalsЗадание