GPU Programming with OpenCL Training Course

Introduction

• Defining OpenCL
• Comparing OpenCL with CUDA and SYCL
• Overview 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 using printf and fprintf functions

OpenCL API

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

OpenCL C

• Understanding the role of OpenCL C in device-side programming
• Writing kernels in OpenCL C to execute on the device and manipulate data
• Using OpenCL C data types, qualifiers, operators, and expressions
• Applying OpenCL C built-in functions, including mathematical, 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
• Working with OpenCL memory spaces, including global, local, constant, and private
• Managing OpenCL memory objects such as buffers, images, and pipes
• Applying OpenCL memory access modes, such as read-only, write-only, and read-write
• Implementing the OpenCL memory consistency model and synchronization mechanisms

OpenCL Execution Model

• Distinguishing 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
• Employing OpenCL work-group functions, including barrier, work_group_reduce, and work_group_scan
• Utilizing OpenCL device functions, such as 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 using 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

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