GPU Programming - OpenCL vs CUDA vs ROCm Training Course

Introduction

• Defining GPU programming.
• The rationale for using GPU programming.
• Challenges and trade-offs inherent to GPU programming.
• Overview of available GPU programming frameworks.
• Selecting the optimal framework for your application.

OpenCL

• Understanding OpenCL.
• Pros and cons of OpenCL.
• Configuring the OpenCL development environment.
• 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 kernels in OpenCL C to execute on the device and manipulate data.
• Leveraging OpenCL built-in functions, variables, and libraries for common operations.
• Utilizing OpenCL memory spaces (global, local, constant, private) to optimize data transfers and memory access.
• Applying 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 coalescing, caching, prefetching, and profiling.

CUDA

• Understanding CUDA.
• Pros and cons of CUDA.
• Setting up the CUDA development environment.
• Creating a basic CUDA program for vector addition.
• Utilizing the CUDA API to query device information, manage memory, copy data, launch kernels, and synchronize threads.
• Writing kernels in CUDA C/C++ to execute on the device and manipulate data.
• Using CUDA built-in functions, variables, and libraries for standard tasks.
• Employing CUDA memory spaces (global, shared, constant, local) to optimize data transfers and memory access.
• Applying the CUDA execution model to control threads, blocks, and grids for parallelism.
• Debugging and testing CUDA programs with tools such as CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimizing CUDA programs using coalescing, caching, prefetching, and profiling.

ROCm

• Understanding ROCm.
• Pros and cons of ROCm.
• Configuring the ROCm development environment.
• Creating a basic ROCm program for vector addition.
• Using the ROCm API to query device information, manage memory, transfer data, launch kernels, and synchronize threads.
• Writing kernels in ROCm C/C++ to execute on the device and manipulate data.
• Leveraging ROCm built-in functions, variables, and libraries for common operations.
• Utilizing ROCm memory spaces (global, local, constant, private) to optimize data transfers and memory access.
• Applying 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 via coalescing, caching, prefetching, and profiling.

Comparison

• Comparing the features, performance, and compatibility of OpenCL, CUDA, and ROCm.
• Assessing GPU programs through benchmarks and metrics.
• Adopting best practices and tips for effective GPU programming.
• Exploring current and future trends and challenges in GPU programming.

Summary and Next Steps