GPU Programming - OpenCL vs CUDA vs ROCm Training Course

Introduction

• Overview of GPU programming
• Benefits and use cases for GPU programming
• Understanding the challenges and trade-offs involved in GPU programming
• The landscape of GPU programming frameworks
• Selecting the appropriate framework for your application

OpenCL

• Introduction to OpenCL
• Advantages and disadvantages of using OpenCL
• Setting up the OpenCL development environment
• Creating a basic OpenCL program to perform vector addition
• Using the OpenCL API to query device info, manage device memory, transfer data, launch kernels, and synchronize threads
• Writing kernels using the OpenCL C language to execute on the device and manipulate data
• Utilizing OpenCL built-in functions, variables, and libraries for common tasks
• Leveraging OpenCL memory spaces (global, local, constant, private) to optimize data transfers and access
• Using the OpenCL execution model to control work-items, work-groups, and ND-ranges for parallelism
• Debugging and testing OpenCL programs with tools like CodeXL
• Optimizing OpenCL programs through coalescing, caching, prefetching, and profiling

CUDA

• Introduction to CUDA
• Advantages and disadvantages of using CUDA
• Setting up the CUDA development environment
• Creating a basic CUDA program for vector addition
• Using the CUDA API to query device info, manage device memory, transfer data, launch kernels, and synchronize threads
• Writing kernels using CUDA C/C++ to execute on the device and manipulate data
• Utilizing CUDA built-in functions, variables, and libraries for common operations
• Leveraging CUDA memory spaces (global, shared, constant, local) to optimize data transfers and access
• Using the CUDA execution model to control 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 coalescing, caching, prefetching, and profiling

ROCm

• Introduction to ROCm
• Advantages and disadvantages of using ROCm
• Setting up the ROCm development environment
• Creating a basic ROCm program for vector addition
• Using the ROCm API to query device info, manage device memory, transfer data, launch kernels, and synchronize threads
• Writing kernels using ROCm C/C++ to execute on the device and manipulate data
• Utilizing ROCm built-in functions, variables, and libraries for common tasks
• Leveraging ROCm memory spaces (global, local, constant, private) to optimize data transfers and access
• Using the ROCm execution model to control threads, blocks, and grids for parallelism
• Debugging and testing ROCm programs with tools like the ROCm Debugger and ROCm Profiler
• Optimizing ROCm programs using coalescing, caching, prefetching, and profiling

Comparison

• Comparing the features, performance, and compatibility of OpenCL, CUDA, and ROCm
• Evaluating GPU program efficiency using benchmarks and metrics
• Key best practices and tips for effective GPU programming
• Exploring current trends and future challenges in GPU programming

Summary and Next Steps