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Course Outline
Introduction
- What is GPU programming?
- Why utilize GPU programming?
- What challenges and trade-offs are associated with GPU programming?
- Which frameworks are available for GPU programming?
- Selecting the appropriate framework for your application
OpenCL
- What is OpenCL?
- What are the pros and cons of OpenCL?
- Configuring the development environment for OpenCL
- Developing a basic OpenCL program for vector addition
- Using the OpenCL API to query device details, manage memory, transfer data, launch kernels, and synchronize threads
- Writing OpenCL C kernels for device execution and data manipulation
- Utilizing OpenCL built-in functions, variables, and libraries for common tasks
- Applying OpenCL memory spaces (global, local, constant, private) to optimize data transfers and memory access
- Using the OpenCL execution model to manage work-items, work-groups, and ND-ranges for parallelism
- Debugging and testing OpenCL programs with tools like CodeXL
- Optimizing OpenCL programs via techniques such as coalescing, caching, prefetching, and profiling
CUDA
- What is CUDA?
- What are the pros and cons of CUDA?
- Configuring the development environment for CUDA
- Developing a basic CUDA program for vector addition
- Using the CUDA API to query device details, manage memory, transfer data, launch kernels, and synchronize threads
- Writing CUDA C/C++ kernels for device execution and data manipulation
- Utilizing CUDA built-in functions, variables, and libraries for common tasks
- Applying CUDA memory spaces (global, shared, constant, local) to optimize data transfers and memory access
- Using the CUDA execution model to manage threads, blocks, and grids for parallelism
- Debugging and testing CUDA programs with tools like CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight
- Optimizing CUDA programs via techniques such as coalescing, caching, prefetching, and profiling
ROCm
- What is ROCm?
- What are the pros and cons of ROCm?
- Configuring the development environment for ROCm
- Developing a basic ROCm program for vector addition
- Using the ROCm API to query device details, manage memory, transfer data, launch kernels, and synchronize threads
- Writing ROCm C/C++ kernels for device execution and data manipulation
- Utilizing ROCm built-in functions, variables, and libraries for common tasks
- Applying ROCm memory spaces (global, local, constant, private) to optimize data transfers and memory access
- Using the ROCm execution model to manage threads, blocks, and grids for parallelism
- Debugging and testing ROCm programs with tools like ROCm Debugger and ROCm Profiler
- Optimizing ROCm programs via techniques such as coalescing, caching, prefetching, and profiling
Comparison
- Comparing the features, performance, and compatibility of OpenCL, CUDA, and ROCm
- 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
- Proficiency in C/C++ programming and understanding of parallel programming concepts
- Fundamental knowledge of computer architecture and memory hierarchy
- Experience using command-line tools and code editors
Audience
- Developers seeking to learn how to use different GPU programming frameworks and compare their features, performance, and compatibility
- Developers aiming to write portable and scalable code compatible with various platforms and devices
- Programmers interested in exploring the trade-offs and challenges inherent in GPU programming and optimization
28 Hours
