GPU Programming with OpenCL Training Course

Introduction

• Introduction to OpenCL.
• Comparing OpenCL with CUDA and SYCL.
• Overview of OpenCL features and architecture.
• Setting up the development environment.

Getting Started

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

OpenCL API

• Understanding the role of the OpenCL API in host programs.
• 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 with the OpenCL API.

OpenCL C

• Understanding the role of OpenCL C in device programs.
• Writing kernels in OpenCL C to execute on the device and manipulate data.
• Using OpenCL C data types, qualifiers, operators, and expressions.
• Utilizing OpenCL C built-in functions, including math, geometric, and relational operations.
• Applying OpenCL C extensions and libraries, such as atomic operations, image handling, and cl_khr_fp16.

OpenCL Memory Model

• Distinguishing between host and device memory models.
• Working with OpenCL memory spaces: 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.
• Implementing the OpenCL memory consistency model and synchronization mechanisms.

OpenCL Execution Model

• Differentiating 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, including barrier, work_group_reduce, and work_group_scan.
• Employing 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 the call stack using the Visual Studio Code debugger.
• Debugging and analyzing OpenCL programs on AMD devices with CodeXL.
• Debugging and analyzing OpenCL programs on Intel devices using Intel VTune.
• Debugging and analyzing OpenCL programs on NVIDIA devices with NVIDIA Nsight.

Optimization

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

Summary and Next Steps