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Duration 28 hours
Course Outline
Introduction
- Overview of OpenCL.
- Comparison of OpenCL with CUDA and SYCL.
- Key features and architectural overview of OpenCL.
- Configuring the development environment.
Getting Started
- Initializing a new OpenCL project in Visual Studio Code.
- Exploring project structure and essential 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.
- Enqueueing commands such as read, write, copy, map, unmap, execute, and wait.
- Managing errors and exceptions within OpenCL applications.
OpenCL C
- Understanding the role of OpenCL C in device programs.
- Writing kernels in OpenCL C to execute on the device and manipulate data.
- Utilizing OpenCL C data types, qualifiers, operators, and expressions.
- Applying OpenCL C built-in functions, including math, geometric, and relational operations.
- Using OpenCL C extensions and libraries, such as atomic, image, 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.
- Utilizing 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 like get_global_id, get_local_id, and get_group_id.
- Applying OpenCL work-group functions such as barrier, work_group_reduce, and work_group_scan.
- Leveraging OpenCL device functions, including get_num_groups, get_global_size, and get_local_size.
Debugging
- Identifying common errors and bugs in OpenCL programs.
- Using the Visual Studio Code debugger to inspect variables, breakpoints, and call stacks.
- 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
- Understanding factors influencing OpenCL program performance.
- Enhancing arithmetic throughput with OpenCL vector data types and vectorization techniques.
- Reducing control overhead and improving locality using loop unrolling and tiling.
- Optimizing memory access and bandwidth using local memory and related functions.
- Measuring and improving execution time and resource utilization through profiling tools.
Summary and Next Steps
Requirements
- A solid understanding of C/C++ programming and parallel computing concepts.
- Fundamental knowledge of computer architecture and memory hierarchy.
- Familiarity with command-line tools and code editors.
Target Audience
- Developers seeking to master OpenCL for programming heterogeneous devices and leveraging parallelism.
- Developers aiming to write portable and scalable code adaptable to various platforms and devices.
- Programmers interested in exploring low-level aspects of heterogeneous programming to optimize code performance.
