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
• Outputting results using printf and fprintf

OpenCL API

• Understanding the role of the OpenCL API 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
• Enqueueing commands such as read, write, copy, map, unmap, execute, and wait via the OpenCL API
• Managing errors and exceptions with the OpenCL API

OpenCL C

• Understanding the role of OpenCL C within the device program
• Writing kernels in OpenCL C to execute on the device and manipulate data
• Using OpenCL C data types, qualifiers, operators, and expressions
• Employing OpenCL C built-in functions for math, geometric, and relational operations
• Integrating OpenCL C extensions and libraries, including atomic, image, and cl_khr_fp16

OpenCL Memory Model

• Distinguishing between host and device memory models
• Utilizing OpenCL memory spaces such as global, local, constant, and private
• Working with OpenCL memory objects including buffers, images, and pipes
• Applying OpenCL memory access modes like 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
• Utilizing OpenCL work-item functions such as get_global_id, get_local_id, and get_group_id
• Applying OpenCL work-group functions like 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
• Inspecting variables, breakpoints, and call stacks using the Visual Studio Code debugger
• Debugging and analyzing OpenCL programs on AMD devices with CodeXL
• Debugging and analyzing OpenCL programs on Intel devices with Intel VTune
• Debugging and analyzing OpenCL programs on NVIDIA devices with NVIDIA Nsight

Optimization

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