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Plan Szkolenia
Introduction
- What is GPU programming?
- Why use GPU programming?
- What are the challenges and trade-offs of GPU programming?
- What are the frameworks and tools for GPU programming?
- Choosing the right framework and tool for your application
OpenCL
- What is OpenCL?
- What are the advantages and disadvantages of OpenCL?
- Setting up the development environment for OpenCL
- Creating a basic OpenCL program that performs vector addition
- Using OpenCL API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads
- Using OpenCL C language to write kernels that execute on the device and manipulate data
- Using OpenCL built-in functions, variables, and libraries to perform common tasks and operations
- Using OpenCL memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses
- Using OpenCL execution model to control the work-items, work-groups, and ND-ranges that define the parallelism
- Debugging and testing OpenCL programs using tools such as CodeXL
- Optimizing OpenCL programs using techniques such as coalescing, caching, prefetching, and profiling
CUDA
- What is CUDA?
- What are the advantages and disadvantages of CUDA?
- Setting up the development environment for CUDA
- Creating a basic CUDA program that performs vector addition
- Using CUDA API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads
- Using CUDA C/C++ language to write kernels that execute on the device and manipulate data
- Using CUDA built-in functions, variables, and libraries to perform common tasks and operations
- Using CUDA memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses
- Using CUDA execution model to control the threads, blocks, and grids that define the parallelism
- Debugging and testing CUDA programs using tools such as CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight
- Optimizing CUDA programs using techniques such as coalescing, caching, prefetching, and profiling
ROCm
- What is ROCm?
- What are the advantages and disadvantages of ROCm?
- Setting up the development environment for ROCm
- Creating a basic ROCm program that performs vector addition
- Using ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads
- Using ROCm C/C++ language to write kernels that execute on the device and manipulate data
- Using ROCm built-in functions, variables, and libraries to perform common tasks and operations
- Using ROCm memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses
- Using ROCm execution model to control the threads, blocks, and grids that define the parallelism
- Debugging and testing ROCm programs using tools such as ROCm Debugger and ROCm Profiler
- Optimizing ROCm programs using techniques such as coalescing, caching, prefetching, and profiling
HIP
- What is HIP?
- What are the advantages and disadvantages of HIP?
- Setting up the development environment for HIP
- Creating a basic HIP program that performs vector addition
- Using HIP language to write kernels that execute on the device and manipulate data
- Using HIP built-in functions, variables, and libraries to perform common tasks and operations
- Using HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses
- Using HIP execution model to control the threads, blocks, and grids that define the parallelism
- Debugging and testing HIP programs using tools such as ROCm Debugger and ROCm Profiler
- Optimizing HIP programs using techniques such as coalescing, caching, prefetching, and profiling
Comparison
- Comparing the features, performance, and compatibility of OpenCL, CUDA, ROCm, and HIP
- Evaluating GPU programs using benchmarks and metrics
- Learning the best practices and tips for GPU programming
- Exploring the current and future trends and challenges of GPU programming
Summary and Next Steps
Wymagania
- An understanding of C/C++ language and parallel programming concepts
- Basic knowledge of computer architecture and memory hierarchy
- Experience with command-line tools and code editors
Audience
- Developers who wish to learn the basics of GPU programming and the main frameworks and tools for developing GPU applications
- Developers who wish to write portable and scalable code that can run on different platforms and devices
- Programmers who wish to explore the benefits and challenges of GPU programming and optimization
21 godzin
Opinie uczestników (5)
Podejście prowadzącego. Bardzo sympatyczny i z poczuciem humoru, jednocześnie z zachowaniem należytego profesjonalizmu. W razie pytań, odpowiedzi były zawsze wyczerpujące. Dla mnie jako osoby mało obeznaną z tematem programowania niskopoziomowego, otwartośc prowadzącego dużo pomoga.
Michał Kwiatek - Nokia Solutions and Networks Sp. z o.o.
Szkolenie - NVIDIA GPU Programming - Extended
zainteresowanie osobami ,które były na szkoleniu, tłumaczenie każdemu z osobna
agata miernik-dukielska - BANK SPÓŁDZIELCZY W OŁAWIE
Szkolenie - Raster and Vector Graphics (Adobe Photoshop, Corel Draw)
wszystko ok
Mariusz Orłowski - Narodowy Bank Polski
Szkolenie - Adobe LiveCycle Designer
praca na przykładach
Kacper Gardziała - Exacto Sp. z o.o.
Szkolenie - Adobe Illustrator
Bardzo interaktywne z różnymi przykładami, z dobrym postępem złożoności między początkiem a końcem szkolenia.
Jenny - Andheo
Szkolenie - GPU Programming with CUDA and Python
Przetłumaczone przez sztuczną inteligencję
