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Duration 28 hours
Course Outline
Introduction
- What is OpenCL?
- Comparing OpenCL, CUDA, and 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 the 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
- Using OpenCL C extensions and libraries, such as atomic, image, and cl_khr_fp16
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, and read-write
- Using the OpenCL memory consistency model 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, and get_group_id
- Using OpenCL work-group functions, such as barrier, work_group_reduce, and work_group_scan
- Using OpenCL device functions, such as get_num_groups, get_global_size, and get_local_size
Debugging
- Understanding common errors and bugs in OpenCL programs
- Using the Visual Studio Code debugger to inspect variables, breakpoints, and call stacks
- 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
Requirements
- Familiarity with C/C++ languages and core parallel programming concepts
- Fundamental understanding of computer architecture and memory hierarchy
- Practical experience with command-line tools and code editors
Target Audience
- Developers seeking to learn how to program heterogeneous devices using OpenCL and exploit their parallelism
- Developers aiming to write portable and scalable code capable of running across diverse platforms and devices
- Programmers interested in exploring the low-level aspects of heterogeneous programming to optimize code performance
