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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 the output using printf and fprintf
OpenCL API
- Understanding the role of OpenCL API in the host program
- Using OpenCL API to query device information and capabilities
- Using OpenCL API to create contexts, command queues, buffers, kernels, and events
- Using OpenCL API to enqueue commands, such as read, write, copy, map, unmap, execute, and wait
- Using 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, relational, etc.
- 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 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 the 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 the common errors and bugs in OpenCL programs
- Using 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 the 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 the execution time and resource utilization
Summary and Next Steps
Requirements
- An understanding of 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
Testimonials (1)
Very interactive with various examples, with a good progression in complexity between the start and the end of the training.
