GPU Programming with OpenCL Training Course

Introduction

• Defining OpenCL and its core concepts.
• Comparing OpenCL with CUDA and SYCL.
• Overview of OpenCL features and architectural design.
• Steps for setting up the development environment.

Getting Started

• Creating a new OpenCL project within Visual Studio Code.
• Exploring the project structure and file organization.
• Compiling and executing the initial program.
• Displaying output results using printf and fprintf functions.

OpenCL API

• 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 API.
• Enqueuing commands such as read, write, copy, map, unmap, execute, and wait.
• Implementing error and exception handling mechanisms with the OpenCL API.

OpenCL C

• The function of OpenCL C in the device-side program.
• Writing kernels in OpenCL C to execute on the device and process data.
• Working with OpenCL C data types, qualifiers, operators, and expressions.
• Utilizing OpenCL C built-in functions for math, geometry, and relational operations.
• Incorporating OpenCL C extensions and libraries, including atomic operations, image processing, 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.
• Managing OpenCL memory objects, including buffers, images, and pipes.
• Applying OpenCL memory access modes like read-only, write-only, and read-write.
• Understanding the OpenCL memory consistency model and synchronization mechanisms.

OpenCL Execution Model

• Differentiating between host and device execution paradigms.
• 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 like barrier, work_group_reduce, and work_group_scan.
• Accessing OpenCL device functions such as get_num_groups, get_global_size, and get_local_size.

Debugging

• Identifying common errors and bugs in OpenCL applications.
• Using the Visual Studio Code debugger to inspect variables, set breakpoints, and analyze the call stack.
• 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

• Key factors influencing the performance of OpenCL programs.
• Improving arithmetic throughput using OpenCL vector data types and vectorization techniques.
• Reducing control overhead and increasing locality through OpenCL loop unrolling and tiling.
• Optimizing memory access and bandwidth using OpenCL local memory and related functions.
• Measuring and enhancing execution time and resource utilization through OpenCL profiling and specialized tools.