GPU Programming with OpenCL Training Course

Introduction

• What is OpenCL?
• Comparison of OpenCL, CUDA, and SYCL
• Overview of OpenCL features and architecture
• Setting up the Development Environment

Getting Started

• Creating a new OpenCL project using Visual Studio Code
• Exploring the project structure and files
• Compiling and running the program
• Displaying output using printf and fprintf

OpenCL API

• Understanding the role of the OpenCL API in the host program
• Querying device information and capabilities using the OpenCL API
• Creating contexts, command queues, buffers, kernels, and events via the OpenCL API
• Enqueuing commands such as read, write, copy, map, unmap, execute, and wait
• Handling errors and exceptions using the OpenCL API

OpenCL C

• Understanding the role of OpenCL C in the device program
• Writing kernels in OpenCL C that execute on the device and manipulate data
• Using OpenCL C data types, qualifiers, operators, and expressions
• Leveraging OpenCL C built-in functions for math, geometric, and relational operations
• Utilizing OpenCL C extensions and libraries, such as atomic, image, and cl_khr_fp16

OpenCL Memory Model

• Understanding the difference between host and device memory models
• Working with OpenCL memory spaces such as global, local, constant, and private
• Managing OpenCL memory objects including buffers, images, and pipes
• Applying OpenCL memory access modes such as read-only, write-only, and read-write
• Utilizing the OpenCL memory consistency model and synchronization mechanisms

OpenCL Execution Model

• Understanding the difference between host and device execution models
• Defining parallelism using OpenCL work-items, work-groups, and ND-ranges
• Using OpenCL work-item functions such as get_global_id, get_local_id, and get_group_id
• Utilizing OpenCL work-group functions such as barrier, work_group_reduce, and work_group_scan
• Using OpenCL device functions such as get_num_groups, get_global_size, and get_local_size

Debugging

• Identifying common errors and bugs in OpenCL programs
• Using the Visual Studio Code debugger to inspect variables, breakpoints, and call stacks
• Debugging and analyzing OpenCL programs on AMD devices using CodeXL
• Debugging and analyzing OpenCL programs on Intel devices using Intel VTune
• Debugging and analyzing OpenCL programs on NVIDIA devices using NVIDIA Nsight

Optimization

• Understanding factors that affect OpenCL program performance
• Improving arithmetic throughput using OpenCL vector data types and vectorization techniques
• Reducing control overhead and increasing locality using OpenCL loop unrolling and tiling techniques
• Optimizing memory access and bandwidth using OpenCL local memory and functions
• Measuring and improving execution time and resource utilization using OpenCL profiling tools