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Course Outline
Introduction
- What is OpenCL?
- OpenCL vs CUDA vs SYCL
- Overview of OpenCL features and architecture.
- Setting up the development environment.
Getting Started
- Creating a new OpenCL project using Visual Studio Code.
- Exploring the project structure and files.
- Compiling and running the program.
- Displaying output using printf and fprintf.
OpenCL API
- Understanding the role of the OpenCL API in the host program.
- Using the OpenCL API to query device information and capabilities.
- Using the OpenCL API to create contexts, command queues, buffers, kernels, and events.
- Using the OpenCL API to enqueue commands such as read, write, copy, map, unmap, execute, and wait.
- Using the OpenCL API to handle errors and exceptions.
OpenCL C
- Understanding the role of OpenCL C in the device program.
- Using OpenCL C to write kernels that execute on the device and manipulate data.
- Using OpenCL C data types, qualifiers, operators, and expressions.
- Using OpenCL C built-in functions such as math, geometric, and relational operations.
- Using OpenCL C extensions and libraries such as atomic, image, cl_khr_fp16, etc.
OpenCL Memory Model
- Understanding the difference between host and device memory models.
- Using OpenCL memory spaces such as global, local, constant, and private.
- Using OpenCL memory objects such as buffers, images, and pipes.
- Using OpenCL memory access modes such as read-only, write-only, read-write, etc.
- Using OpenCL memory consistency models and synchronization mechanisms.
OpenCL Execution Model
- Understanding the difference between host and device execution models.
- Using OpenCL work-items, work-groups, and ND-ranges to define parallelism.
- Using OpenCL work-item functions such as get_global_id, get_local_id, get_group_id, etc.
- Using OpenCL work-group functions such as barrier, work_group_reduce, work_group_scan, etc.
- Using OpenCL device functions such as get_num_groups, get_global_size, get_local_size, etc.
Debugging
- Understanding common errors and bugs in OpenCL programs.
- Using the Visual Studio Code debugger to inspect variables, breakpoints, call stack, etc.
- Using CodeXL to debug and analyze OpenCL programs on AMD devices.
- Using Intel VTune to debug and analyze OpenCL programs on Intel devices.
- Using NVIDIA Nsight to debug and analyze OpenCL programs on NVIDIA devices.
Optimization
- Understanding factors that affect the performance of OpenCL programs.
- Using OpenCL vector data types and vectorization techniques to improve arithmetic throughput.
- Using OpenCL loop unrolling and loop tiling techniques to reduce control overhead and increase locality.
- Using OpenCL local memory and local memory functions to optimize memory accesses and bandwidth.
- Using OpenCL profiling and profiling tools to measure and improve execution time and resource utilization.
Summary and Next Steps
Requirements
- Understanding of the C/C++ language and parallel programming concepts.
- Basic knowledge of computer architecture and memory hierarchy.
- Experience with command-line tools and code editors.
Audience
- Developers who wish to learn how to use OpenCL to program heterogeneous devices and exploit their parallelism.
- Developers who wish to write portable and scalable code that can run on different platforms and devices.
- Programmers who wish to explore the low-level aspects of heterogeneous programming and optimize their code performance.
28 Hours
