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Duration 28 hours
Course Outline
Introduction
- Definition and scope of OpenCL
- 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 file organization
- Compiling and executing the program
- Displaying output using printf and fprintf
OpenCL API
- Understanding the role of the OpenCL API in host-side programming
- 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
- Handling errors and exceptions within the OpenCL API
OpenCL C
- Understanding the role of OpenCL C in device-side programming
- Writing kernels to execute on the device and manipulate data using OpenCL C
- Utilizing OpenCL C data types, qualifiers, operators, and expressions
- Employing OpenCL C built-in functions for math, geometry, and relational operations
- Using OpenCL C extensions and libraries, including atomic, image, and cl_khr_fp16
OpenCL Memory Model
- Distinguishing between host and device memory models
- Utilizing OpenCL memory spaces: global, local, constant, and private
- Working with OpenCL memory objects such as buffers, images, and pipes
- Applying OpenCL memory access modes like read-only, write-only, and read-write
- Understanding OpenCL memory consistency and synchronization mechanisms
OpenCL Execution Model
- Distinguishing 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
- Employing OpenCL work-group functions like barrier, work_group_reduce, and work_group_scan
- Utilizing 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 call stacks using the Visual Studio Code debugger
- 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 that influence OpenCL program performance
- Improving arithmetic throughput using OpenCL vector data types and vectorization
- Reducing control overhead and increasing locality through loop unrolling and tiling
- Optimizing memory access and bandwidth using OpenCL local memory and functions
- Measuring and improving execution time and resource utilization via OpenCL profiling tools
Requirements
- Familiarity with C/C++ language and parallel programming principles
- Fundamental understanding of computer architecture and memory hierarchy
- Proficiency with command-line utilities and code editors
Target Audience
- Developers aiming to utilize OpenCL for programming heterogeneous devices and exploiting parallelism
- Developers seeking to write portable, scalable code capable of running across various platforms and devices
- Programmers interested in exploring the low-level aspects of heterogeneous programming to optimize code performance
