GPU Programming with OpenCL Training Course

Introduction

• Definition and scope of OpenCL.
• Comparative analysis of OpenCL, CUDA, and SYCL.
• High-level overview of OpenCL features and architectural components.
• Configuration of the development environment.

Getting Started

• Initiating a new OpenCL project in Visual Studio Code.
• Navigation through the project structure and file organization.
• Process of compiling and executing the application.
• Output display methods using printf and fprintf.

OpenCL API

• The function of the OpenCL API within host applications.
• Querying device capabilities and information via the OpenCL API.
• Instantiating contexts, command queues, buffers, kernels, and events using the API.
• Enqueuing commands such as read, write, copy, map, unmap, execute, and wait operations.
• Strategies for handling errors and exceptions in OpenCL applications.

OpenCL C

• The role of OpenCL C within device-side programs.
• Authoring kernels in OpenCL C to execute data manipulation on the device.
• Application of OpenCL C data types, qualifiers, operators, and expressions.
• Utilization of built-in functions for mathematical, geometric, and relational operations.
• Integration of extensions and libraries, including atomic operations, image processing, and cl_khr_fp16.

OpenCL Memory Model

• Distinguishing between host and device memory architectures.
• Application of memory spaces such as global, local, constant, and private memory.
• Management of memory objects including buffers, images, and pipes.
• Implementation of memory access modes like read-only, write-only, and read-write.
• Understanding the memory consistency model and synchronization protocols.

OpenCL Execution Model

• Contrast between host and device execution paradigms.
• Defining parallelism through work-items, work-groups, and ND-ranges.
• Use of work-item functions such as get_global_id, get_local_id, and get_group_id.
• Application of work-group functions including barrier, work_group_reduce, and work_group_scan.
• Utilization of device functions like get_num_groups, get_global_size, and get_local_size.

Debugging

• Identification of common errors and bugs in OpenCL code.
• Inspection of 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 using Intel VTune.
• Debugging and analyzing OpenCL programs on NVIDIA devices with NVIDIA Nsight.

Optimization

• Key factors influencing OpenCL program performance.
• Enhancing arithmetic throughput via vector data types and vectorization strategies.
• Reducing control overhead and improving locality through loop unrolling and tiling.
• Optimizing memory bandwidth and access patterns using local memory and related functions.
• Measuring and improving execution time and resource utilization through OpenCL profiling tools.