GPU Programming - OpenCL vs CUDA vs ROCm Training Course

Introduction

• Defining GPU programming
• The benefits of adopting GPU programming
• Understanding the challenges and trade-offs involved in GPU programming
• Overview of available frameworks for GPU programming
• Selecting the appropriate framework for your specific application

OpenCL

• An overview of OpenCL
• Advantages and disadvantages of using OpenCL
• Configuring the development environment for OpenCL
• Developing a basic OpenCL application for vector addition
• Using the OpenCL API to query device info, manage memory, transfer data, launch kernels, and synchronize threads
• Using OpenCL C to write kernels that execute on the device and manipulate data
• Utilizing OpenCL built-in functions, variables, and libraries for common operations
• Optimizing data transfers and memory access using OpenCL memory spaces (global, local, constant, private)
• Managing parallelism using the OpenCL execution model (work-items, work-groups, and ND-ranges)
• Debugging and testing OpenCL programs with tools like CodeXL
• Optimizing OpenCL programs through coalescing, caching, prefetching, and profiling

CUDA

• An overview of CUDA
• Advantages and disadvantages of using CUDA
• Configuring the development environment for CUDA
• Developing a basic CUDA application for vector addition
• Using the CUDA API to query device info, manage memory, transfer data, launch kernels, and synchronize threads
• Using CUDA C/C++ to write kernels that execute on the device and manipulate data
• Utilizing CUDA built-in functions, variables, and libraries for common operations
• Optimizing data transfers and memory access using CUDA memory spaces (global, shared, constant, local)
• Managing parallelism using the CUDA execution model (threads, blocks, and grids)
• Debugging and testing CUDA programs with tools like CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight
• Optimizing CUDA programs through coalescing, caching, prefetching, and profiling

ROCm

• An overview of ROCm
• Advantages and disadvantages of using ROCm
• Configuring the development environment for ROCm
• Developing a basic ROCm application for vector addition
• Using the ROCm API to query device info, manage memory, transfer data, launch kernels, and synchronize threads
• Using ROCm C/C++ to write kernels that execute on the device and manipulate data
• Utilizing ROCm built-in functions, variables, and libraries for common operations
• Optimizing data transfers and memory access using ROCm memory spaces (global, local, constant, private)
• Managing parallelism using the ROCm execution model (threads, blocks, and grids)
• Debugging and testing ROCm programs with tools like ROCm Debugger and ROCm Profiler
• Optimizing ROCm programs through coalescing, caching, prefetching, and profiling

Comparative Analysis

• Comparing the features, performance, and compatibility of OpenCL, CUDA, and ROCm
• Evaluating GPU programs using benchmarks and key metrics
• Best practices and expert tips for GPU programming
• Exploring current and future trends and challenges in GPU programming

Summary and Next Steps