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Course Outline
Introduction
- What constitutes GPU programming?
- Why is GPU programming beneficial?
- What are the challenges and trade-offs associated with GPU programming?
- Which frameworks and tools are available for GPU programming?
- How to choose 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 the OpenCL API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
- Using the OpenCL C language to write kernels that execute on the device and manipulate data.
- Utilizing 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 the OpenCL execution model to control work-items, work-groups, and ND-ranges that define 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 the CUDA API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
- Using the CUDA C/C++ language to write kernels that execute on the device and manipulate data.
- Utilizing 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 the CUDA execution model to control threads, blocks, and grids that define 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 the ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
- Using the ROCm C/C++ language to write kernels that execute on the device and manipulate data.
- Utilizing 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 the ROCm execution model to control threads, blocks, and grids that define 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 the HIP language to write kernels that execute on the device and manipulate data.
- Utilizing 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 the HIP execution model to control threads, blocks, and grids that define 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 best practices and tips for GPU programming.
- Exploring current and future trends and challenges in GPU programming.
Summary and Next Steps
Requirements
- A solid understanding of the C/C++ language and parallel programming concepts.
- Basic knowledge of computer architecture and memory hierarchy.
- Experience using 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 aiming to write portable and scalable code that can run on different platforms and devices.
- Programmers interested in exploring the benefits and challenges of GPU programming and optimization.
21 Hours
