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Duration 28 hours
Course Outline
Introduction
- Understanding what OpenCL is
- Comparing OpenCL with CUDA and SYCL
- Overview of OpenCL features and architectural design
- Setting up the development environment
Getting Started
- Creating 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 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
- Handling errors and exceptions through the OpenCL API
OpenCL C
- Understanding the role of OpenCL C in device-side programming
- Writing kernels for device execution and data manipulation
- Utilizing OpenCL C data types, qualifiers, operators, and expressions
- Applying built-in functions for math, geometry, and relational operations
- Using extensions and libraries, including atomic operations, image processing, and cl_khr_fp16
OpenCL Memory Model
- Distinguishing between host and device memory models
- Working with memory spaces: global, local, constant, and private
- Managing memory objects such as buffers, images, and pipes
- Configuring memory access modes like read-only, write-only, and read-write
- Applying the memory consistency model and synchronization mechanisms
OpenCL Execution Model
- Distinguishing between host and device execution models
- Defining parallelism using work-items, work-groups, and ND-ranges
- Using work-item functions like get_global_id, get_local_id, and get_group_id
- Applying work-group functions such as barrier, work_group_reduce, and work_group_scan
- Leveraging device functions like get_num_groups, get_global_size, and get_local_size
Debugging
- Identifying common errors and bugs in OpenCL programs
- Inspecting 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 with Intel VTune
- Debugging and analyzing OpenCL programs on NVIDIA devices with NVIDIA Nsight
Optimization
- Understanding factors influencing OpenCL program performance
- Improving arithmetic throughput via vector data types and vectorization
- Reducing control overhead and increasing locality using loop unrolling and tiling
- Optimizing memory access and bandwidth with local memory and related functions
- Measuring and improving execution time and resource utilization through profiling and tools
Requirements
- Familiarity with C/C++ programming and core concepts of parallel computing.
- Foundational knowledge of computer architecture and memory hierarchies.
- Proficiency with command-line interfaces and code editors.
Target Audience
- Developers seeking to master OpenCL for heterogeneous device programming and parallelism exploitation.
- Engineers aiming to write portable, scalable code capable of running across various platforms and devices.
- Programmers interested in exploring low-level heterogeneous programming aspects and optimizing code performance.
