GPU Programming - OpenCL vs CUDA vs ROCm Training Course

Introduction

• Defining GPU programming and its core principles.
• The strategic advantages of adopting GPU programming.
• Understanding the challenges and trade-offs involved in GPU development.
• Overview of major GPU programming frameworks.
• Criteria for selecting the most suitable framework for specific applications.

OpenCL

• An in-depth look at OpenCL.
• Analyzing the strengths and limitations of OpenCL.
• Configuring the development environment for OpenCL.
• Building a foundational OpenCL application for vector addition.
• Utilizing the OpenCL API for device queries, memory management, data transfer, kernel launch, and thread synchronization.
• Writing device-executed kernels and manipulating data using OpenCL C.
• Leveraging OpenCL built-in functions, variables, and libraries for common operations.
• Optimizing data transfers and memory access via OpenCL memory spaces (global, local, constant, private).
• Controlling parallelism through the OpenCL execution model (work-items, work-groups, ND-ranges).
• Debugging and testing OpenCL applications using tools like CodeXL.
• Applying optimization techniques such as coalescing, caching, prefetching, and profiling to OpenCL code.

CUDA

• Understanding the CUDA ecosystem.
• Evaluating the benefits and drawbacks of using CUDA.
• Setting up the CUDA development environment.
• Developing a basic CUDA application for vector addition.
• Using the CUDA API to manage device information, memory, data transfer, kernel execution, and synchronization.
• Authoring kernels and manipulating data using the CUDA C/C++ language.
• Utilizing CUDA built-in functions, variables, and libraries for standard tasks.
• Optimizing performance using CUDA memory spaces (global, shared, constant, local).
• Managing parallelism via the CUDA execution model (threads, blocks, grids).
• Debugging and testing CUDA programs with tools like CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimizing CUDA applications through coalescing, caching, prefetching, and profiling.

ROCm

• Introduction to the ROCm platform.
• Assessing the advantages and disadvantages of ROCm.
• Configuring the development environment for ROCm.
• Creating a basic ROCm application for vector addition.
• Using the ROCm API for device queries, memory management, data transfer, kernel launch, and synchronization.
• Writing kernels and manipulating data using the ROCm C/C++ language.
• Employing ROCm built-in functions, variables, and libraries for common operations.
• Optimizing data flow using ROCm memory spaces (global, local, constant, private).
• Controlling parallelism through the ROCm execution model (threads, blocks, grids).
• Debugging and testing ROCm programs using the ROCm Debugger and Profiler.
• Applying optimization techniques such as coalescing, caching, prefetching, and profiling to ROCm code.

Comparative Analysis

• Direct comparison of features, performance, and compatibility among OpenCL, CUDA, and ROCm.
• Evaluating GPU program efficiency using benchmarks and key metrics.
• Best practices and expert tips for effective GPU programming.
• Exploring current trends and future challenges in the GPU programming landscape.

Summary and Next Steps