GPU Programming with OpenCL Training Course

Introduction

• Overview of OpenCL
• Comparing OpenCL, CUDA, and SYCL
• Summary of OpenCL features and architectural design
• Configuring the Development Environment

Getting Started

• Initiating a new OpenCL project in Visual Studio Code
• Examining the project structure and associated files
• Compiling and executing the program
• Displaying output via printf and fprintf

OpenCL API

• The role of the OpenCL API within host programs
• Querying device information and capabilities via the OpenCL API
• Instantiating contexts, command queues, buffers, kernels, and events with the OpenCL API
• Enqueueing commands such as read, write, copy, map, unmap, execute, and wait
• Managing errors and exceptions using the OpenCL API

OpenCL C

• The function of OpenCL C in device-side programming
• Authoring kernels in OpenCL C for device execution and data manipulation
• Working with OpenCL C data types, qualifiers, operators, and expressions
• Employing OpenCL C built-in functions, including math, geometric, and relational operations
• Utilizing OpenCL C extensions and libraries, such as atomic, image, and cl_khr_fp16

OpenCL Memory Model

• Distinguishing between host and device memory models
• Utilizing OpenCL memory spaces: global, local, constant, and private
• Managing OpenCL memory objects like buffers, images, and pipes
• Applying OpenCL memory access modes, including read-only, write-only, and read-write
• Implementing OpenCL memory consistency models and synchronization mechanisms

OpenCL Execution Model

• Contrasting host and device execution models
• Defining parallelism using OpenCL work-items, work-groups, and ND-ranges
• Using OpenCL work-item functions like get_global_id, get_local_id, and get_group_id
• Leveraging OpenCL work-group functions such as barrier, work_group_reduce, and work_group_scan
• Applying OpenCL device functions, including get_num_groups, get_global_size, and get_local_size

Debugging

• Identifying common errors and bugs in OpenCL applications
• Inspecting variables, breakpoints, and call stacks with the Visual Studio Code debugger
• 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 influencing OpenCL program performance
• Enhancing arithmetic throughput using OpenCL vector data types and vectorization techniques
• Reducing control overhead and improving locality via loop unrolling and loop tiling
• Optimizing memory access and bandwidth using local memory and associated functions
• Measuring and improving execution time and resource utilization through OpenCL profiling tools