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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 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
- Handling errors and exceptions using the OpenCL API
OpenCL C
- Understanding the role of OpenCL C in the device program
- Writing kernels that execute on the device and manipulate data using OpenCL C
- Using OpenCL C data types, qualifiers, operators, and expressions
- Utilizing OpenCL C built-in functions, such as math, geometric, and relational functions
- Leveraging OpenCL C extensions and libraries, such as atomic operations, image processing, and cl_khr_fp16
OpenCL Memory Model
- Understanding the differences between host and device memory models
- Using OpenCL memory spaces, such as global, local, constant, and private
- Working with OpenCL memory objects, including buffers, images, and pipes
- Utilizing OpenCL memory access modes such as read-only, write-only, and read-write
- Applying the OpenCL memory consistency model and synchronization mechanisms
OpenCL Execution Model
- Understanding the differences 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
- Utilizing OpenCL work-group functions like 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
- Identifying common errors and bugs in OpenCL programs
- Using the Visual Studio Code debugger to inspect variables, set breakpoints, and view the call stack
- 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 impact OpenCL program performance
- Improving arithmetic throughput using OpenCL vector data types and vectorization techniques
- Reducing control overhead and increasing locality using loop unrolling and loop tiling techniques
- Optimizing memory accesses and bandwidth using OpenCL local memory and related functions
- Measuring and improving execution time and resource utilization using OpenCL profiling and tools
Summary and Next Steps
Requirements
- Proficiency in the C/C++ language and concepts of parallel programming
- Foundational knowledge of computer architecture and memory hierarchy
- Experience with command-line tools and code editors
Audience
- Developers looking to learn how to program heterogeneous devices using OpenCL and exploit their parallelism
- Developers aiming to write portable and scalable code capable of running on diverse platforms and devices
- Programmers interested in exploring the low-level aspects of heterogeneous programming and optimizing code performance
28 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 6400 € + VAT*
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